代码搜索：Extraction

找到约 5,483 项符合「Extraction」的源代码

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txt readme.txt

Dialgo Telephone Answering Machine v1.0 Lite ============================================ Features : Desktop Based Voice Answering Machine with Caller ID Extraction, Digit Monitoring, Wave Playb
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www.eeworm.com/read/152702/12091974

acf st_mult1.acf

-- -- Copyright (C) 1988-2001 Altera Corporation -- Any megafunction design, and related net list (encrypted or decrypted), -- support information, device programming or simulation file, and any
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www.eeworm.com/read/388251/8622797

hpp extractlc.hpp

/** * @file ExtractLC.hpp * This class eases LC combination data extraction from a RinexObsData object. */ #ifndef ExtractLC_GPSTK #define ExtractLC_GPSTK //=====================================
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hpp extractpc.hpp

/** * @file ExtractPC.hpp * This class eases PC combination data extraction from a RinexObsData object. */ #ifndef ExtractPC_GPSTK #define ExtractPC_GPSTK //=====================================
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m plsm.m

% PLSM Partial Least Squares Feature Extraction % % W = PLSM % W = PLSM([],MAXLV,METHOD) % % [W, INFORM] = PLSM(A,MAXLV,METHOD) % % INPUT % A training dataset % MAXLV maxim
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m~ contents.m~

% Linear transformations for feature extraction. % % lda - Linear Discriminant Analysis. % linproj - Linear data projection. % pca - Principal Component Analysis. % % About: Statistica
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m~ contents.m~

% Linear transformations for feature extraction. % % lda - Linear Discriminant Analysis. % linproj - Linear data projection. % pca - Principal Component Analysis. % % About: Statistica
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m~ contents.m~

% Linear transformations for feature extraction. % % lda - Linear Discriminant Analysis. % linproj - Linear data projection. % pca - Principal Component Analysis. % % About: Statistica
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www.eeworm.com/read/419967/10825652

java dextractor.java

// The class that handles the main data extraction window, plus a // few helper classes // // Copyright (c) 2000, 2004 Markus Demleitner // This program is free software; you can redistribute it and/
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www.eeworm.com/read/299984/7140341

m plsm.m

% PLSM Partial Least Squares Feature Extraction % % W = PLSM % W = PLSM([],MAXLV,METHOD) % % [W, INFORM] = PLSM(A,MAXLV,METHOD) % % INPUT % A training dataset % MAXLV maxim
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