代码搜索：classification

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texi libbow-desc.texi

@samp{Libbow} is a library of C code intended for writing statistical text-processing programs. This distribution includes the library, as well as a text classification front-end, and a document retr
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www.eeworm.com/read/259886/11759400

m demop6.m

%% Linearly Non-separable Vectors % A 2-input hard limit neuron fails to properly classify 5 input vectors because % they are linearly non-separable. % % Copyright 1992-2002 The MathWorks, Inc.
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m demop4.m

%% Outlier Input Vectors % A 2-input hard limit neuron is trained to classify 5 input vectors into two % categories. However, because 1 input vector is much larger than all of the % others, traini
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www.eeworm.com/read/155781/11848318

readme

Libsvm is a simple, easy-to-use, and efficient software for SVM classification and regression. It solves C-SVM classification, nu-SVM classification, one-class-SVM, epsilon-SVM regression, and nu-SVM
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www.eeworm.com/read/155041/11902198

readme

README ======================================================================= Incremental and decremental support vector machine learning Matlab code, data and demos G. Cauwenberghs gert@jhu.edu ====
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www.eeworm.com/read/154717/11932370

readme

Libsvm is a simple, easy-to-use, and efficient software for SVM classification and regression. It solves C-SVM classification, nu-SVM classification, one-class-SVM, epsilon-SVM regression, and nu-SVM
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www.eeworm.com/read/343594/11940653

m elm_fun.m

function [TrainingTime, TrainingAccuracy, TestingAccuracy] = elm_fun(TrainingData_File, TestingData_File, NumberofHiddenNeurons, ActivationFunction, Elm_Type) % Usage: elm(TrainingData_File, Testin
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www.eeworm.com/read/154122/11988697

m contents.m

% Support Vector Machine Toolbox % Version 2.0-Aug-1998 % % Support Vector Classification % % svc - Calculate support vectors for classification % svcplot - Plot 2 dimensional clas
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www.eeworm.com/read/342008/12046789

m classd.m

%CLASSD Classify data using a given classifier % % labels = classd(D) % % Finds the labels of the classified dataset D (typically the result % of a mapping or classification A*W). For each object
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www.eeworm.com/read/255755/12057253

m labeld.m

%LABELD Find labels of classification dataset (perform crisp classification) % % LABELS = LABELD(Z) % LABELS = Z*LABELD % LABELS = LABELD(A,W) % LABELS = A*W*LABELD % LABELS = LABELD(Z,THRE
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