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Revision 512010-10-01 - CyrilAllauzen

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OpenKernel Library

OpenKernel is a library for creating, combining and using

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  This library is being developed by C. Allauzen, M. Mohri and A. Rostamizadeh. It is intended to be comprehensive, flexible, efficient and scale well to large-scale problems. It is an open source project distributed under the Apache licenseExternal site. This work has been partially supported by Google Inc.
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NEW New version available now, includes algorithms for learning kernels and kernel ridge regression.
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Red led Latest version includes algorithms for learning kernels and kernel ridge regression.
 
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Revision 502010-04-09 - CyrilAllauzen

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OpenKernel Library

OpenKernel is a library for creating, combining and using

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  This library is being developed by C. Allauzen, M. Mohri and A. Rostamizadeh. It is intended to be comprehensive, flexible, efficient and scale well to large-scale problems. It is an open source project distributed under the Apache licenseExternal site. This work has been partially supported by Google Inc.
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ALERT! New version available now, includes algorithms for learning kernels and kernel ridge regression.
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NEW New version available now, includes algorithms for learning kernels and kernel ridge regression.
 
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  • Creating and using rational kernels
 
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Revision 492010-01-11 - CyrilAllauzen

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OpenKernel Library

OpenKernel is a library for creating, combining and using

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  This library is being developed by C. Allauzen, M. Mohri and A. Rostamizadeh. It is intended to be comprehensive, flexible, efficient and scale well to large-scale problems. It is an open source project distributed under the Apache licenseExternal site. This work has been partially supported by Google Inc.
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ALERT! New version coming in January 2010. Will include algorithms for learning kernels and kernel ridge regression.
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ALERT! New version available now, includes algorithms for learning kernels and kernel ridge regression.
 

Revision 482010-01-08 - CyrilAllauzen

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OpenKernel Library

OpenKernel is a library for creating, combining and using

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  This library is being developed by C. Allauzen, M. Mohri and A. Rostamizadeh. It is intended to be comprehensive, flexible, efficient and scale well to large-scale problems. It is an open source project distributed under the Apache licenseExternal site. This work has been partially supported by Google Inc.
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ALERT! New version coming soon. Will include algorithms for learning kernels and kernel ridge regression.
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ALERT! New version coming in January 2010. Will include algorithms for learning kernels and kernel ridge regression.
 

Revision 472009-08-13 - CyrilAllauzen

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OpenKernel Library

OpenKernel is a library for creating, combining and using

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  This library is being developed by C. Allauzen, M. Mohri and A. Rostamizadeh. It is intended to be comprehensive, flexible, efficient and scale well to large-scale problems. It is an open source project distributed under the Apache licenseExternal site. This work has been partially supported by Google Inc.
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Work in progress, under construction New version coming soon. Will include algorithms for learning kernels and kernel ridge regression.
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ALERT! New version coming soon. Will include algorithms for learning kernels and kernel ridge regression.
 

Revision 462009-08-13 - CyrilAllauzen

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OpenKernel Library

OpenKernel is a library for creating, combining and using kernels for machine learning applications. The current focus of library is on rational kernels. It is based on the OpenFst library.

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This library is being developed by C. Allauzen, M. Mohri and A. Rostami. It is intended to be comprehensive, flexible, efficient and scale well to large-scale problems. It is an open source project distributed under the Apache licenseExternal site. This work has been partially supported by Google Inc.
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This library is being developed by C. Allauzen, M. Mohri and A. Rostamizadeh. It is intended to be comprehensive, flexible, efficient and scale well to large-scale problems. It is an open source project distributed under the Apache licenseExternal site. This work has been partially supported by Google Inc.
  Work in progress, under construction New version coming soon. Will include algorithms for learning kernels and kernel ridge regression.

Revision 452009-08-12 - CyrilAllauzen

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OpenKernel Library

OpenKernel is a library for creating, combining and using

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  This library is being developed by C. Allauzen, M. Mohri and A. Rostami. It is intended to be comprehensive, flexible, efficient and scale well to large-scale problems. It is an open source project distributed under the Apache licenseExternal site. This work has been partially supported by Google Inc.
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Work in progress, under construction New version coming soon. Will include algorithms for learning kernels and kernel ridge regression.
 

Revision 442009-08-11 - CyrilAllauzen

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OpenKernel Library

OpenKernel is a library for creating, combining and using kernels for machine learning applications. The current focus of library is on rational kernels. It is based on the OpenFst library.

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This library was developed by C. Allauzen and M. Mohri. It is intended to be comprehensive, flexible, efficient and scale well to large-scale problems. It is an open source project distributed under the Apache licenseExternal site. This work has been partially supported by Google Inc.
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>
This library is being developed by C. Allauzen, M. Mohri and A. Rostami. It is intended to be comprehensive, flexible, efficient and scale well to large-scale problems. It is an open source project distributed under the Apache licenseExternal site. This work has been partially supported by Google Inc.
 

Revision 432009-06-17 - CyrilAllauzen

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OpenKernel Library

OpenKernel is a library for creating, combining and using kernels for machine learning applications. The current focus of library is on rational kernels. It is based on the OpenFst library.

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This library was developed by C. Allauzen and M. Mohri. It is intended to be comprehensive, flexible, efficient and scale well to large-scale problems. It is an open source project distributed under the Apache licenseExternal site. This work has been partially supported by Google Inc.
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>
This library was developed by C. Allauzen and M. Mohri. It is intended to be comprehensive, flexible, efficient and scale well to large-scale problems. It is an open source project distributed under the Apache licenseExternal site. This work has been partially supported by Google Inc.
 

Revision 422008-08-14 - CyrilAllauzen

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OpenKernel Library

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ALERT! Under construction.
 OpenKernel is a library for creating, combining and using kernels for machine learning applications. The current focus of library is on rational kernels. It is based on the OpenFst library.

Revision 412007-10-31 - CyrilAllauzen

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OpenKernel Library

ALERT! Under construction.

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 kernels for machine learning applications. The current focus of library is on rational kernels. It is based on the OpenFst library.
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This library was developed by C. Allauzen and M. Mohri. It is intended to be comprehensive, flexible, efficient and scale well to large problems. It is an open source project distributed under the Apache licenseExternal site.
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This library was developed by C. Allauzen and M. Mohri. It is intended to be comprehensive, flexible, efficient and scale well to large-scale problems. It is an open source project distributed under the Apache licenseExternal site. This work has been partially supported by Google Inc.
 

Revision 402007-10-30 - CyrilAllauzen

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OpenKernel Library

ALERT! Under construction.

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Revision 392007-10-30 - CyrilAllauzen

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OpenKernel Library

ALERT! Under construction.

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Revision 382007-10-29 - CyrilAllauzen

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OpenKernel Library

ALERT! Under construction.

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 This library was developed by C. Allauzen and M. Mohri. It is intended to be comprehensive, flexible, efficient and scale well to large problems. It is an open source project distributed under the Apache licenseExternal site.
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Revision 372007-10-22 - CyrilAllauzen

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OpenKernel Library

ALERT! Under construction.

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META FILEATTACHMENT attr="" autoattached="1" comment="" date="1191871321" name="openfst.jpg" path="openfst.jpg" size="14965" user="Main.MichaelRiley" version="11"

Revision 362007-10-19 - CyrilAllauzen

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OpenKernel Library

ALERT! Under construction.

OpenKernel is a library for creating, combining and using

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rational kernels. It is based on the OpenFst library.
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kernels for machine learning applications. The current focus of library is on rational kernels. It is based on the OpenFst library.
  This library was developed by C. Allauzen and M. Mohri. It is intended to be comprehensive, flexible, efficient and scale well to large problems. It is an open source project distributed under the Apache licenseExternal site.

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  • Download (ALERT! coming soon)

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META FILEATTACHMENT attachment="openfst.jpg" attr="" comment="" date="1181798274" name="openfst.jpg" path="openfst.jpg" size="14965" stream="openfst.jpg" user="Main.MichaelRiley" version="11"
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META FILEATTACHMENT attr="" autoattached="1" comment="" date="1191871321" name="openfst.jpg" path="openfst.jpg" size="14965" user="Main.MichaelRiley" version="11"

Revision 352007-10-09 - CyrilAllauzen

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OpenKernel Library

ALERT! Under construction.

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This library was developed at NYU's Courant Institute (C. Allauzen, M. Mohri). It is intended to be comprehensive, flexible, efficient and scale well to large problems. It is an open source project distributed under the Apache licenseExternal site.
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This library was developed by C. Allauzen and M. Mohri. It is intended to be comprehensive, flexible, efficient and scale well to large problems. It is an open source project distributed under the Apache licenseExternal site.
 

Revision 342007-10-09 - CyrilAllauzen

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OpenKernel Library

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Warning, important Under construction.
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ALERT! Under construction.
  OpenKernel is a library for creating, combining and using rational kernels. It is based on the OpenFst library.
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This library was developed at NYU's Courant Institute (C. Allauzen, M. Mohri). It is intended to be comprehensive, flexible, efficient and scale well to large problems. It is an open source project distributed under the Apache licenseExternal site.
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This library was developed at NYU's Courant Institute (C. Allauzen, M. Mohri). It is intended to be comprehensive, flexible, efficient and scale well to large problems. It is an open source project distributed under the Apache licenseExternal site.
 
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Revision 332007-10-09 - CyrilAllauzen

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OpenKernel Library

Warning, important Under construction.

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This library was developed at NYU's Courant Institute (C. Allauzen, M. Mohri). It is intended to be comprehensive, flexible, efficient and scale well to large problems. It is an open source project distributed under the Apache license.
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This library was developed at NYU's Courant Institute (C. Allauzen, M. Mohri). It is intended to be comprehensive, flexible, efficient and scale well to large problems. It is an open source project distributed under the Apache licenseExternal site.
 

Revision 322007-10-08 - CyrilAllauzen

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OpenKernel Library

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Warning, important Under construction.
 OpenKernel is a library for creating, combining and using rational kernels. It is based on the OpenFst library.
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 This library was developed at NYU's Courant Institute (C. Allauzen, M. Mohri). It is intended to be comprehensive, flexible, efficient and scale well to large problems. It is an open source project distributed under the Apache license.

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META FILEATTACHMENT attachment="openfst.jpg" attr="" comment="" date="1181798274" name="openfst.jpg" path="openfst.jpg" size="14965" stream="openfst.jpg" user="Main.MichaelRiley" version="11"

Revision 312007-10-08 - CyrilAllauzen

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OpenFst Library

>
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OpenKernel Library

OpenKernel is a library for creating, combining and using rational kernels. It is based on the OpenFst library.

This library was developed at NYU's Courant Institute (C. Allauzen, M. Mohri). It is intended to be comprehensive, flexible, efficient and scale well to large problems. It is an open source project distributed under the Apache license.

 
Deleted:
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OpenFst is a library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs). Weighted finite-state transducers are automata where each transition has an input label, an output label, and a weight. The more familiar finite-state acceptor is represented as a transducer with each transition's input and output label equal. Finite-state acceptors are used to represent sets of strings (specifically, regular or rational sets); finite-state transducers are used to represent binary relations between pairs of strings (specifically, rational transductions). The weights can be used to represent the cost of taking a particular transition.

FSTs have key applications in speech recognition and synthesis, machine translation, optical character recognition, pattern matching, string processing, machine learning, information extraction and retrieval among others. Often a weighted transducer is used to represent a probabilistic model (e.g., an n-gram model, pronunciation model). FSTs can be optimized by determinization and minimization, models can be applied to hypothesis sets (also represented as automata) or cascaded by finite-state composition, and the best results can be selected by shortest-path algorithms.

This library was developed at Google Research (M. Riley, J. Schalkwyk, W. Skut) and NYU's Courant Institute (C. Allauzen, M. Mohri). It is intended to be comprehensive, flexible, efficient and scale well to large problems. It is an open source project distributed under the Apache license.

 
META FILEATTACHMENT attachment="openfst.jpg" attr="" comment="" date="1181798274" name="openfst.jpg" path="openfst.jpg" size="14965" stream="openfst.jpg" user="Main.MichaelRiley" version="11"

Revision 302007-07-12 - MichaelRiley

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OpenFst Library

OpenFst is a library for constructing, combining, optimizing, and

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-- CyrilAllauzen
-- MichaelRiley
 
META FILEATTACHMENT attachment="openfst.jpg" attr="" comment="" date="1181798274" name="openfst.jpg" path="openfst.jpg" size="14965" stream="openfst.jpg" user="Main.MichaelRiley" version="11"

Revision 292007-07-12 - MichaelRiley

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OpenFst Library

OpenFst is a library for constructing, combining, optimizing, and

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-- CyrilAllauzen

Revision 282007-07-11 - MichaelRiley

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OpenFst Library

OpenFst is a library for constructing, combining, optimizing, and

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 models can be applied to hypothesis sets (also represented as automata) or cascaded by finite-state composition, and the best results can be selected by shortest-path algorithms.
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This library was developed at Google Research (M. Riley, J. Schalkwyk, W. Skut) and NYU's Courant Institute
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This library was developed at Google Research (M. Riley, J. Schalkwyk, W. Skut) and NYU's Courant Institute
 (C. Allauzen, M. Mohri). It is intended to be comprehensive, flexible, efficient and scale well to large problems. It is an open source project distributed under the Apache license.

Revision 272007-07-06 - CyrilAllauzen

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OpenFst Library

OpenFst is a library for constructing, combining, optimizing, and

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 by finite-state composition, and the best results can be selected by shortest-path algorithms.

This library was developed at Google Research (M. Riley, J. Schalkwyk, W. Skut) and NYU's Courant Institute

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(C. Allauzen, M. Mohri). It is intended to be comprehensive, flexible, efficient and scale well to large problems.. It is an open source project distributed under the Apache license.
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(C. Allauzen, M. Mohri). It is intended to be comprehensive, flexible, efficient and scale well to large problems. It is an open source project distributed under the Apache license.
 

Revision 262007-07-05 - CyrilAllauzen

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OpenFst Library

OpenFst is a library for constructing, combining, optimizing, and

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 probabilistic model (e.g., an n-gram model, pronunciation model). FSTs can be optimized by determinization and minimization, models can be applied to hypothesis sets (also represented as automata) or cascaded
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by finite-state composition, and the best results can be selected by shortest-path algorithms.
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by finite-state composition, and the best results can be selected by shortest-path algorithms.
  This library was developed at Google Research (M. Riley, J. Schalkwyk, W. Skut) and NYU's Courant Institute (C. Allauzen, M. Mohri). It is intended to be comprehensive, flexible, efficient and scale well to large problems.. It is an open source project distributed under the Apache license.

Revision 252007-07-04 - MichaelRiley

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OpenFst Library

OpenFst is a library for constructing, combining, optimizing, and

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 by finite-state composition, and the best results can be selected by shortest-path algorithms.

This library was developed at Google Research (M. Riley, J. Schalkwyk, W. Skut) and NYU's Courant Institute

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(C. Allauzen, M. Mohri). It is an open source project distributed under the Apache license.

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(C. Allauzen, M. Mohri). It is intended to be comprehensive, flexible, efficient and scale well to large problems.. It is an open source project distributed under the Apache license.
 

Revision 242007-06-21 - MichaelRiley

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OpenFst Library

OpenFst is a library for constructing, combining, optimizing, and

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 string processing, machine learning, information extraction and retrieval among others. Often a weighted transducer is used to represent a probabilistic model (e.g., an n-gram model,
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pronunciation model). FSTs can be optimized by determinization and minimization,
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pronunciation model). FSTs can be optimized by determinization and minimization,
 models can be applied to hypothesis sets (also represented as automata) or cascaded by finite-state composition, and the best results can be selected by shortest-path algorithms.

Revision 232007-06-16 - MichaelRiley

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OpenFst Library

OpenFst is a library for constructing, combining, optimizing, and

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Revision 222007-06-16 - MichaelRiley

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OpenFst Library

OpenFst is a library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs). Weighted finite-state transducers are automata where each transition has an

Changed:
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input label, an output label, and a weight. The more familiar
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input label, an output label, and a weight. The more familiar
 finite-state acceptor is represented as a transducer with each transition's input and output label equal. Finite-state acceptors are used to represent sets of strings (specifically, regular or
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 probabilistic model (e.g., an n-gram model, pronunciation model). FSTs can be optimized by determinization and minimization, models can be applied to hypothesis sets (also represented as automata) or cascaded
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by finite-state composition, and the best results can be selected by shortest-path algorithms.
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by finite-state composition, and the best results can be selected by shortest-path algorithms.
  This library was developed at Google Research (M. Riley, J. Schalkwyk, W. Skut) and NYU's Courant Institute (C. Allauzen, M. Mohri). It is an open source project distributed under the Apache license.

Revision 212007-06-15 - CyrilAllauzen

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OpenFst Library

OpenFst is a library for constructing, combining, optimizing, and

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Revision 202007-06-15 - MichaelRiley

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OpenFst Library

OpenFst is a library for constructing, combining, optimizing, and

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Revision 192007-06-14 - MichaelRiley

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OpenFst Library

OpenFst is a library for constructing, combining, optimizing, and

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 finite-state transducers are automata where each transition has an input label, an output label, and a weight. The more familiar finite-state acceptor is represented as a transducer with each
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transition's input and output the same. Finite-state acceptors
>
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transition's input and output label equal. Finite-state acceptors
 are used to represent sets of strings (specifically, regular or rational sets); finite-state transducers are used to represent binary relations between pairs of strings (specifically, rational transductions). The weights can be used to represent the cost of taking a particular transition.
Changed:
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FSTs have been applied to a variety of problems in speech and text processing, OCR, and information retrieval. Typically a weighted transducer is used to represent either hypothesis sets (e.g., word lattices) or probabilistic models (e.g., ngram models, pronunciation models). The FSTs can be optimized by determinization and minimization, models can be applied to hypothesis sets via finite-state composition, and the best results can be selected by shortest-path algorithms.
>
>
FSTs have key applications in speech recognition and synthesis, machine translation, optical character recognition, pattern matching, string processing, machine learning, information extraction and retrieval among others. Often a weighted transducer is used to represent a probabilistic model (e.g., an n-gram model, pronunciation model). FSTs can be optimized by determinization and minimization, models can be applied to hypothesis sets (also represented as automata) or cascaded by finite-state composition, and the best results can be selected by shortest-path algorithms.
 
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This library was developed at Google Research (M. Riley, J. Schalkwyk, W. Skut) and NYU's Courant Institute (C. Allauzen, M. Mohri).
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This library was developed at Google Research (M. Riley, J. Schalkwyk, W. Skut) and NYU's Courant Institute (C. Allauzen, M. Mohri). It is an open source project distributed under the Apache license.
 

Revision 182007-06-14 - MichaelRiley

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OpenFst Library

OpenFst is a library for constructing, combining, optimizing, and

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META FILEATTACHMENT attachment="openfst.jpg" attr="" comment="" date="1181798274" name="openfst.jpg" path="openfst.jpg" size="14965" stream="openfst.jpg" user="Main.MichaelRiley" version="11"

Revision 172007-06-14 - MichaelRiley

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OpenFst Library

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 -- CyrilAllauzen - 25 May 2007
-- MichaelRiley - 25 May 2007
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META FILEATTACHMENT attachment="openfst.jpg" attr="" comment="" date="1181798274" name="openfst.jpg" path="openfst.jpg" size="14965" stream="openfst.jpg" user="Main.MichaelRiley" version="11"

Revision 162007-06-14 - MichaelRiley

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OpenFst Library

 openfst.jpg
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OpenFst Library

 OpenFst is a library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs). Weighted finite-state transducers are automata where each transition has an

Revision 152007-06-14 - MichaelRiley

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OpenFst Library

openfst.jpg

Revision 142007-06-13 - MichaelRiley

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OpenFst Library

openfst.jpg

Revision 132007-06-13 - MichaelRiley

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OpenFst Library

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This is a library for constructing, combining, optimizing, and
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openfst.jpg

OpenFst is a library for constructing, combining, optimizing, and

 searching weighted finite-state transducers (FSTs). Weighted finite-state transducers are automata where each transition has an input label, an output label, and a weight. The more familiar
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 binary relations between pairs of strings (specifically, rational transductions). The weights can be used to represent the cost of taking a particular transition.
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FSTs have been applied to a variety of problems in speech, natural language processing and information retrieval. Typically a weighted transducer is used to represent either hypothesis sets (e.g., speech lattices) or probabilistic models (e.g., language models or pronunciation models). The FSTs can be optimized by determinization and minimization,
>
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FSTs have been applied to a variety of problems in speech and text processing, OCR, and information retrieval. Typically a weighted transducer is used to represent either hypothesis sets (e.g., word lattices) or probabilistic models (e.g., ngram models, pronunciation models). The FSTs can be optimized by determinization and minimization,
 models can be applied to hypothesis sets via finite-state composition, and the best results can be selected by shortest-path algorithms.
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This library was developed at Google Research and NYU's Courant Institute.
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This library was developed at Google Research (M. Riley, J. Schalkwyk, W. Skut) and NYU's Courant Institute (C. Allauzen, M. Mohri).
 

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  -- CyrilAllauzen - 25 May 2007
-- MichaelRiley - 25 May 2007
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META FILEATTACHMENT attachment="openfst.jpg" attr="" comment="" date="1181757714" name="openfst.jpg" path="openfst.jpg" size="20182" stream="openfst.jpg" user="Main.MichaelRiley" version="10"

Revision 122007-05-26 - MichaelRiley

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OpenFst Library

This is a library for constructing, combining, optimizing, and

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 This library was developed at Google Research and NYU's Courant Institute.

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Available Information

 

Revision 112007-05-25 - MichaelRiley

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OpenFst Library

This is a library for constructing, combining, optimizing, and

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 models can be applied to hypothesis sets via finite-state composition, and the best results can be selected by shortest-path algorithms.

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This library was developed at Google Research and NYU's Courant Institute.

 

Available Information

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-- CyrilAllauzen - 25 May 2007
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Revision 102006-12-12 - CyrilAllauzen

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OpenFst Library

This is a library for constructing, combining, optimizing, and

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Kernel Web Utilities

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Revision 92006-12-12 - CyrilAllauzen

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OpenFst Library

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OpenFst Library

  This is a library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs). Weighted
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Kernel Web Utilities

Revision 82006-12-12 - MichaelRiley

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  This is a library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs). Weighted
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FST Library

This is a library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs). Weighted finite-state transducers are automata where each transition has an input label, an output label, and a weight. The more familiar finite-state acceptor is represented as a transducer with each transition's input and output the same. Finite-state acceptors are used to represent sets of strings (specifically, regular or rational sets); finite-state transducers are used to represent binary relations between pairs of strings (specifically, rational transductions). The weights can be used to represent the cost of taking a particular transition.

FSTs have been applied to a variety of problems in speech, natural language processing and information retrieval. Typically a weighted transducer is used to represent either hypothesis sets (e.g., speech lattices) or probabilistic models (e.g., language models or pronunciation models). The FSTs can be optimized by determinization and minimization, models can be applied to hypothesis sets via finite-state composition, and the best results can be selected by shortest-path algorithms.

 

Available Information

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Kernel Web Utilities

Revision 62005-03-28 - TWikiContributor

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Revision 42002-04-14 - PeterThoeny

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Revision 32002-04-07 - PeterThoeny

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Revision 22001-11-24 - PeterThoeny

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Revision 12001-08-08 - PeterThoeny

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