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Fix README and Contents typos #54

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28 changes: 14 additions & 14 deletions Contents.m
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Expand Up @@ -18,7 +18,7 @@
% CHAPTER03
% linReg - Fit linear regression model y=w'x+w0
% linRegFp - Fit empirical Bayesian linear model with Mackay fixed point method (p.168)
% linRegPred - Compute linear regression model reponse y = w'*X+w0 and likelihood
% linRegPred - Compute linear regression model response y = w'*X+w0 and likelihood
% linRnd - Generate data from a linear model p(t|w,x)=G(w'x+w0,sigma), sigma=sqrt(1/beta)
% CHAPTER04
% binPlot - Plot binary classification result for 2d data
Expand All @@ -27,7 +27,7 @@
% logitBinPred - Prediction of binary logistic regression model
% logitMn - Multinomial regression for multiclass problem (Multinomial likelihood)
% logitMnPred - Prediction of multiclass (multinomial) logistic regression model
% sigmoid - Sigmod function
% sigmoid - Sigmoid function
% softmax - Softmax function
% CHAPTER05
% mlpClass - Train a multilayer perceptron neural network for classification with backpropagation
Expand All @@ -39,7 +39,7 @@
% knCenter - Centerize the data in the kernel space
% knGauss - Gaussian (RBF) kernel K = exp(-|x-y|/(2s));
% knKmeans - Perform kernel kmeans clustering.
% knKmeansPred - Prediction for kernel kmeans clusterng
% knKmeansPred - Prediction for kernel kmeans clustering
% knLin - Linear kernel (inner product)
% knPca - Kernel PCA
% knPcaPred - Prediction for kernel PCA
Expand All @@ -49,9 +49,9 @@
% sd2kn - Transform a squared distance matrix to a kernel matrix.
% CHAPTER07
% rvmBinFp - Relevance Vector Machine (ARD sparse prior) for binary classification.
% rvmBinPred - Prodict the label for binary logistic regression model
% rvmBinPred - Predict the label for binary logistic regression model
% rvmRegFp - Relevance Vector Machine (ARD sparse prior) for regression
% rvmRegPred - Compute RVM regression model reponse y = w'*X+w0 and likelihood
% rvmRegPred - Compute RVM regression model response y = w'*X+w0 and likelihood
% rvmRegSeq - Sparse Bayesian Regression (RVM) using sequential algorithm
% CHAPTER08
% MRF
Expand All @@ -61,13 +61,13 @@
% mrfIsGa - Contruct a latent Ising MRF with Gaussian observation
% mrfMf - Mean field for MRF
% NaiveBayes
% nbBern - Naive bayes classifier with indepenet Bernoulli.
% nbBern - Naive bayes classifier with independent Bernoulli.
% nbBernPred - Prediction of naive Bayes classifier with independent Bernoulli.
% nbGauss - Naive bayes classifier with indepenet Gaussian
% nbGauss - Naive bayes classifier with independent Gaussian
% nbGaussPred - Prediction of naive Bayes classifier with independent Gaussian.
% CHAPTER09
% kmeans - Perform kmeans clustering.
% kmeansPred - Prediction for kmeans clusterng
% kmeansPred - Prediction for kmeans clustering
% kmeansRnd - Generate samples from a Gaussian mixture distribution with common variances (kmeans model).
% kmedoids - Perform k-medoids clustering.
% kseeds - Perform kmeans++ seeding
Expand All @@ -76,7 +76,7 @@
% mixBernRnd - Generate samples from a Bernoulli mixture distribution.
% mixGaussEm - Perform EM algorithm for fitting the Gaussian mixture model.
% mixGaussPred - Predict label and responsibility for Gaussian mixture model.
% mixGaussRnd - Genarate samples form a Gaussian mixture model.
% mixGaussRnd - Generate samples from a Gaussian mixture model.
% rvmBinEm - Relevance Vector Machine (ARD sparse prior) for binary classification.
% rvmRegEm - Relevance Vector Machine (ARD sparse prior) for regression
% CHAPTER10
Expand All @@ -94,21 +94,21 @@
% mixDpGb - Collapsed Gibbs sampling for Dirichlet process (infinite) mixture model.
% mixDpGbOl - Online collapsed Gibbs sampling for Dirichlet process (infinite) mixture model.
% mixGaussGb - Collapsed Gibbs sampling for Dirichlet process (infinite) Gaussian mixture model (a.k.a. DPGM).
% mixGaussSample - Genarate samples form a Gaussian mixture model with GaussianWishart prior.
% mixGaussSample - Generate samples from a Gaussian mixture model with GaussianWishart prior.
% CHAPTER12
% fa - Perform EM algorithm for factor analysis model
% pca - Principal component analysis
% pcaEm - Perform EM-like algorithm for PCA (by Sam Roweis).
% pcaEmC - Perform Constrained EM like algorithm for PCA.
% ppcaEm - Perform EM algorithm to maiximize likelihood of probabilistic PCA model.
% ppcaEm - Perform EM algorithm to maximize likelihood of probabilistic PCA model.
% ppcaRnd - Generate data from probabilistic PCA model
% ppcaVb - Perform variatioanl Bayeisan inference for probabilistic PCA model.
% ppcaVb - Perform variational Bayesian inference for probabilistic PCA model.
% CHAPTER13
% HMM
% hmmEm - EM algorithm to fit the parameters of HMM model (a.k.a Baum-Welch algorithm)
% hmmFilter - HMM forward filtering algorithm.
% hmmRnd - Generate a data sequence from a hidden Markov model.
% hmmSmoother - HMM smoothing alogrithm (normalized forward-backward or normalized alpha-beta algorithm).
% hmmSmoother - HMM smoothing algorithm (normalized forward-backward or normalized alpha-beta algorithm).
% hmmViterbi - Viterbi algorithm (calculated in log scale to improve numerical stability).
% LDS
% kalmanFilter - Kalman filter (forward algorithm for linear dynamic system)
Expand All @@ -119,7 +119,7 @@
% CHAPTER14
% adaboostBin - Adaboost for binary classification (weak learner: kmeans)
% adaboostBinPred - Prediction of binary Adaboost
% mixLinPred - Prediction function for mxiture of linear regression
% mixLinPred - Prediction function for mixture of linear regression
% mixLinReg - Mixture of linear regression
% mixLinRnd - Generate data from mixture of linear model
% mixLogitBin - Mixture of logistic regression model for binary classification optimized by Newton-Raphson method
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10 changes: 5 additions & 5 deletions README.md
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Expand Up @@ -5,14 +5,14 @@ Pattern Recognition and Machine Learning by C. Bishop ([PRML](http://research.mi

It is written purely in Matlab language. It is self-contained. There is no external dependency.

Note: this package requires Matlab **R2016b** or latter, since it utilizes a new Matlab syntax called [Implicit expansion](https://cn.mathworks.com/help/matlab/release-notes.html?rntext=implicit+expansion&startrelease=R2016b&endrelease=R2016b&groupby=release&sortby=descending) (a.k.a. broadcasting). It also requires Statistics Toolbox (for some simple random number generator) and Image Processing Toolbox (for reading image data).
Note: this package requires Matlab **R2016b** or later, since it utilizes a new Matlab syntax called [Implicit expansion](https://cn.mathworks.com/help/matlab/release-notes.html?rntext=implicit+expansion&startrelease=R2016b&endrelease=R2016b&groupby=release&sortby=descending) (a.k.a. broadcasting). It also requires Statistics Toolbox (for some simple random number generator) and Image Processing Toolbox (for reading image data).

Design Goal
-------
* Succinct: The code is extremely compact. Minimizing code length is a major goal. As a result, the core of the algorithms can be easily spotted.
* Efficient: Many tricks for speeding up Matlab code are applied (e.g. vectorization, matrix factorization, etc.). Usually, functions in this package are orders faster than Matlab builtin ones (e.g. kmeans).
* Robust: Many tricks for numerical stability are applied, such as computing probability in logrithm domain, square root matrix update to enforce matrix symmetry\PD, etc.
* Readable: The code is heavily commented. Corresponding formulas in PRML are annoted. Symbols are in sync with the book.
* Robust: Many tricks for numerical stability are applied, such as computing probability in logarithm domain, square root matrix update to enforce matrix symmetry\PD, etc.
* Readable: The code is heavily commented. Corresponding formulas in PRML are annotated. Symbols are in sync with the book.
* Practical: The package is not only readable, but also meant to be easily used and modified to facilitate ML research. Many functions in this package are already widely used (see [Matlab file exchange](http://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A49739)).

Installation
Expand All @@ -25,9 +25,9 @@ git clone https://github.com/PRML/PRMLT.git

3. Run some demos in ~/PRMLT/demo folder. Enjoy!

FeedBack
Feedback
-------
If you find any bug or have any suggestion, please do file issues. I am graceful for any feedback and will do my best to improve this package.
If you find any bug or have any suggestion, please do file issues. I am grateful for any feedback and will do my best to improve this package.

License
-------
Expand Down