gaussian.out

来自「高斯混合模型算法」· OUT 代码 · 共 28 行

\BOOKMARK [1][-]{section.1}{Gaussian statistics}{}
\BOOKMARK [2][-]{subsection.1.1}{Samples from a Gaussian density}{section.1}
\BOOKMARK [3][-]{subsubsection.1.1.1}{Experiment:}{subsection.1.1}
\BOOKMARK [2][-]{subsection.1.2}{Gaussian modeling: Mean and variance of a sample}{section.1}
\BOOKMARK [3][-]{subsubsection.1.2.1}{Experiment:}{subsection.1.2}
\BOOKMARK [3][-]{subsubsection.1.2.2}{Example:}{subsection.1.2}
\BOOKMARK [3][-]{subsubsection.1.2.3}{Question:}{subsection.1.2}
\BOOKMARK [2][-]{subsection.1.3}{Likelihood of a sample with respect to a Gaussian model}{section.1}
\BOOKMARK [3][-]{subsubsection.1.3.1}{Question:}{subsection.1.3}
\BOOKMARK [3][-]{subsubsection.1.3.2}{Experiment:}{subsection.1.3}
\BOOKMARK [3][-]{subsubsection.1.3.3}{Example:}{subsection.1.3}
\BOOKMARK [1][-]{section.2}{Statistical pattern recognition}{}
\BOOKMARK [2][-]{subsection.2.1}{A-priori class probabilities}{section.2}
\BOOKMARK [3][-]{subsubsection.2.1.1}{Experiment:}{subsection.2.1}
\BOOKMARK [3][-]{subsubsection.2.1.2}{Example:}{subsection.2.1}
\BOOKMARK [2][-]{subsection.2.2}{Gaussian modeling of classes}{section.2}
\BOOKMARK [3][-]{subsubsection.2.2.1}{Example:}{subsection.2.2}
\BOOKMARK [2][-]{subsection.2.3}{Bayesian classification}{section.2}
\BOOKMARK [3][-]{subsubsection.2.3.1}{Experiment:}{subsection.2.3}
\BOOKMARK [3][-]{subsubsection.2.3.2}{Example:}{subsection.2.3}
\BOOKMARK [2][-]{subsection.2.4}{Discriminant surfaces}{section.2}
\BOOKMARK [3][-]{subsubsection.2.4.1}{Experiment:}{subsection.2.4}
\BOOKMARK [3][-]{subsubsection.2.4.2}{Question:}{subsection.2.4}
\BOOKMARK [1][-]{section.3}{Unsupervised training}{}
\BOOKMARK [2][-]{subsection.3.1}{K-means algorithm}{section.3}
\BOOKMARK [2][-]{subsection.3.2}{Viterbi-EM algorithm for Gaussian clustering}{section.3}
\BOOKMARK [2][-]{subsection.3.3}{EM algorithm for Gaussian clustering}{section.3}