文件名称:svm_perf.tar.gz
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SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X --> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
,SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X--> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
h: X --> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
,SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X--> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
相关搜索: svm pe
binary classification
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下载文件列表
LICENSE.txt
Makefile
svm_struct_api.h
svm_struct_api.c
svm_struct_api_types.h
svm_struct_learn_custom.c
svm_struct/Makefile
svm_struct/svm_struct_main.c
svm_struct/svm_struct_learn.h
svm_struct/svm_struct_learn.c
svm_struct/svm_struct_common.h
svm_struct/svm_struct_common.c
svm_struct/svm_struct_classify.c
svm_light/LICENSE.txt
svm_light/Makefile
svm_light/svm_learn.c
svm_light/kernel.h
svm_light/svm_learn.h
svm_light/svm_learn_main.c
svm_light/svm_classify.c
svm_light/svm_loqo.c
svm_light/svm_common.c
svm_light/svm_common.h
svm_light/svm_hideo.c
Makefile
svm_struct_api.h
svm_struct_api.c
svm_struct_api_types.h
svm_struct_learn_custom.c
svm_struct/Makefile
svm_struct/svm_struct_main.c
svm_struct/svm_struct_learn.h
svm_struct/svm_struct_learn.c
svm_struct/svm_struct_common.h
svm_struct/svm_struct_common.c
svm_struct/svm_struct_classify.c
svm_light/LICENSE.txt
svm_light/Makefile
svm_light/svm_learn.c
svm_light/kernel.h
svm_light/svm_learn.h
svm_light/svm_learn_main.c
svm_light/svm_classify.c
svm_light/svm_loqo.c
svm_light/svm_common.c
svm_light/svm_common.h
svm_light/svm_hideo.c
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