prml笔记-notes on pattern recognition and machine learning

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1、PRML笔记NotesonPatternRecognitionandMachineLearning(Bishop)Version1.0①JianXiao目录Checklist.....................................................................................................2Chapter1Introduction................................................................................4Ch

2、apter2ProbabilityDistribution............................................................10..................................................14Chapter3LinearModelsforRegression..............................................19Chapter4LinearModelsforClassificationChapter5NeuralNetworks.........

3、.............................................................26Chapter6Kernelmethods........................................................................33Chapter7SparseKernelMachine............................................................39Chapter8GraphicalModels......................

4、...............................................47Chapter9MixtureModelsandEM..........................................................53Chapter10ApproximateInference...........................................................58Chapter11SamplingMethod............................................

5、.......................63Chapter12ContinuousLatentVariables..................................................68Chapter13SequentialData......................................................................72Chapter14CombiningModels..............................................................

6、...74①iamxiaojian@gmail.comChecklistFrequentist-Bayesian对峙构成的主要内容Frequentist版本Bayesian版本解模型所用的方法LinearbasisfunctionBayesianlinearbasisfunction前者和后者皆有closed-formregressionregressionsolutionLogisticregressionBayesianlogitsticregression前者牛顿迭代(IRLS)，后者LaplaceapproximationNeuralnetwork(forBayesia

7、nNeuralnetwork(for前者gradientdecent，后者regression,classification)regression,classification)LaplaceapproximationSVM(forregression,RVM(forregression,前者解二次规划，后者迭代、classification)classification)LaplaceapproximationGaussianmixturemodelBayesianGaussianmixt