mlpy机器学习库的介绍

mlpy机器学习库的介绍

1、Introduce

mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and the GNU Scientific Libraries.

mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is Open Source, distributed under the GNU General Public License version 3.

2、Documentation

(1)online Documentation:http://mlpy.sourceforge.net/docs/3.5/

(2)PDF Documentation:https://sourceforge.net/projects/mlpy/files/mlpy%203.5.0/

3、CONTENTS

2 Introduction 
	2.1 Conventions 
3 Tutorial 
	3.1 Tutorial 1 - Iris Dataset 
4 Linear Methods for Regression 
	4.1 Ordinary Least Squares 
	4.2 Ridge Regression 
	4.3 Partial Least Squares 
	4.4 Last Angle Regression (LARS) 
	4.5 Elastic Net 
5 Linear Methods for Classification 
	5.1 Linear Discriminant Analysis Classifier (LDAC) 
	5.2 Basic Perceptron 
	5.3 Elastic Net Classifier 
	5.4 Logistic Regression
	5.5 Support Vector Classification 
	5.6 Diagonal Linear Discriminant Analysis (DLDA) 
	5.7 Golub Classifier 

6 Kernels 
	6.1 Kernel Functions 
	6.2 Kernel Classes 
	6.3 Functions 
	6.4 Centering in Feature Space 
	6.5 Make a Custom Kernel 
7 Non Linear Methods for Regression 
	7.1 Kernel Ridge Regression 
	7.2 Support Vector Regression 
8 Non Linear Methods for Classification 
	8.1 Parzen-based classifier
	8.2 Support Vector Classification 
	8.3 Kernel Fisher Discriminant Classifier 
	8.4 k-Nearest-Neighbor 
	8.5 Classification Tree 
	8.6 Maximum Likelihood Classifier 
9 Support Vector Machines (SVMs) 
	9.1 Support Vector Machines from [LIBSVM]
	9.2 Kernel Adatron 
10 Large Linear Classification from [LIBLINEAR] 

11 Cluster Analysis 
	11.1 Hierarchical Clustering 
	11.2 Memory-saving Hierarchical Clustering
	11.3 k-means 
12 Algorithms for Feature Weighting 
	12.1 Iterative RELIEF
13 Feature Selection 
	13.1 Recursive Feature Elimination
14 Dimensionality Reduction 
	14.1 Linear Discriminant Analysis (LDA) 
	14.2 Spectral Regression Discriminant Analysis (SRDA) 
	14.3 Kernel Fisher Discriminant Analysis (KFDA)
	14.4 Principal Component Analysis (PCA) 
	14.5 Fast Principal Component Analysis (PCAFast)
	14.6 Kernel Principal Component Analysis (KPCA)
15 Cross Validation 
	15.1 Leave-one-out and k-fold
	15.2 Random Subsampling (aka MonteCarlo)
	15.3 All Combinations
16 Metrics 
	16.1 Classification 
	16.2 Regression
17 A Set of Statistical Functions 

18 Canberra Distances and Stability Indicator of Ranked Lists 
	18.1 Canberra distance 
	18.2 Canberra Distance with Location Parameter 
	18.3 Canberra Stability Indicator
19 Borda Count 

20 Find Peaks 

21 Dynamic Time Warping (DTW) 
	21.1 Standard DTW 
	21.2 Subsequence DTW 
22 Longest Common Subsequence (LCS) 
	22.1 Standard LCS 
	22.2 LCS for real series
23 mlpy.wavelet - Wavelet Transform 
	23.1 Padding 
	23.2 Discrete Wavelet Transform
	23.3 Undecimated Wavelet Transform
	23.4 Continuous Wavelet Transform 
24 Short Guide to Centering and Scaling