代码搜索：Classify

找到约 2,639 项符合「Classify」的源代码

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java incrementalclassifiertrainer.java

package edu.umass.cs.mallet.base.classify; import edu.umass.cs.mallet.base.types.InstanceList; /** * Adds the notion of incremental training to a ClassifierTrainer, through the * availability of
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java sumconditionalobjectivefunction.java

package edu.stanford.nlp.classify; import java.util.*; import edu.stanford.nlp.optimization.*; /** @author Dan Klein */ public class SumConditionalObjectiveFunction extends LogConditionalObjective
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java logconditionalobjectivefunction.java

package edu.stanford.nlp.classify; import java.util.*; import edu.stanford.nlp.optimization.*; /** @author Dan Klein */ public class LogConditionalObjectiveFunction extends AbstractCachingDiffFunc
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java incrementalclassifiertrainer.java

package edu.umass.cs.mallet.base.classify; import edu.umass.cs.mallet.base.types.InstanceList; /** * Adds the notion of incremental training to a ClassifierTrainer, through the * availability of
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scala higherkind_novalue.scala

abstract class HigherKind[m[s]] { val x: m // type of kind *->* doesn't classify a value, but a val/def/... can only contain/return a value def y: m }
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m parzen.m

function D = parzen(train_features, train_targets, hn, region) % Classify using the Parzen windows algorithm % Inputs: % features - Train features % targets - Train targets % hn - No
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m ml_diag.m

function D = ML_diag(train_features, train_targets, AlgorithmParameters, region) % Classify using the maximum likelyhood algorithm with diagonal covariance matrices % Inputs: % features - Train
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m multivariate_splines.m

function D = Multivariate_Splines(train_features, train_targets, params, region) % Classify using multivariate adaptive regression splines % Inputs: % features - Train features % targets -
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m deterministic_boltzmann.m

function D = Deterministic_Boltzmann(train_features, train_targets, params, region); % Classify using the deterministic Boltzmann algorithm % Inputs: % features - Train features % targets - Tra
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m backpropagation_quickprop.m

function [D, Wh, Wo] = Backpropagation_Quickprop(train_features, train_targets, params, region) % Classify using a backpropagation network with a batch learning algorithm and quickprop % Inputs:
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