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/*! \page methods 机器学习方法

目前Shogun的机器学习功能分为几个部分：feature表示，feature预处理，
核函数表示,核函数标准化，距离表示，分类器表示，聚类方法，分布，
性能评价方法，回归方法，结构化输出学习器。以下是shogun已实现的机器学习
相关算法和类。

  \section featrep_sec Feature表示
Shogun提供多种feature表示。它们分别是：简单feature（参照CSimpleFeatures），它们是标准的二维
矩阵；字符串feature（参照CStringFeatures），它们其实是一个包含多个字符串的列表，每个字符串的
长度不限；稀疏feature（参照CSparseFeatures），它们用于表示稀疏矩阵。

  每一种对象

  \li Simple Features (CSimpleFeatures)
  \li Strings (CStringFeatures)
  \li Sparse Features (CSparseFeatures)

  支持下面这些数据类型:

  \li bool
  \li 8bit char
  \li 8bit Byte
  \li 16bit Integer
  \li 16bit Word
  \li 32bit Integer
  \li 32bit Unsigned Integer
  \li 32bit Float matrix
  \li 64bit Float matrix
  \li 96bit Float matrix

  另外还有其它的feature类型。其中有些是基于上面的三种基本的feature类型，如CTOPFeatures
  （CHMM中使用的TOP Kernel features），CFKFeatures（CHMM使用的Fisher Kernel features）
  和CRealFileFeatures（从一个二进制文件获取向量）。请注意，所有feature类型都继承于
  CFeatures。更加复杂的类型还有
  \li CAttributeFeatures - Features of attribute value pairs.
  \li CCombinedDotFeatures -  Features that allow stacking of dot features.
  \li CCombinedFeatures - Features that allow stacking of arbitrary features.
  \li CDotFeatures - Features that support a certain set of features (like multiplication with a scalar + addition to a dense vector). Examples are sparse and dense features.
  \li CDummyFeatures - Features without content; Only number of vectors is known.
  \li CExplicitSpecFeatures - Implement spectrum kernel feature space explicitly.
  \li CImplicitWeightedSpecFeatures - DotFeatures that implicitly implement weighted spectrum kernel features.
  \li CWDFeatures - DotFeatures that implicitly implement weighted degree kernel features.

另外，label由CLabels表示，字母表由CAlphabet表示。


  \section preproc_sec 预处理器
  前面提到的所在feature类型都可以作预处理，如减去均值或将向量范数标准化为1等。以下是已实现的预处理器：
  \li CNormOne - Normalizes vectors to norm 1.
  \li CLogPlusOne - add 1 and applies log().
  \li CPCACut - Keeps eigenvectors with the highest eigenvalues.
  \li CPruneVarSubMean - removes dimensions with little variance, substracting the mean.
  \li CSortUlongString - Sorts vectors.
  \li CSortWordString - Sorts vectors.



  \section classifiers_sec 分类器

在shogun中实现了一系列分类器。它们中有些是标准的二类分类器，有些是一类分类器，有
些是多类分类器。它们中有一部分是线性分类器和SVM。较快的线性SVM分类器有CSGD,
CSVMOcas及CLibLinear，它们能处理上百万的样本及feature。

  \subsection linclassi_sec 线性分类器
  \li CPerceptron - standard online perceptron
  \li CLDA - fishers linear discriminant
  \li CLPM - linear programming machine (1-norm regularized SVM)
  \li CLPBoost - linear programming machine using boosting on the features
  \li CSVMPerf - a linear svm with l2-regularized bias
  \li CLibLinear - a linear svm with l2-regularized bias
  \li CSVMLin - a linear svm with l2-regularized bias
  \li CSVMOcas - a linear svm with l2-regularized bias
  \li CSubgradientSVM - SVM based on steepest subgradient descent
  \li CSubgradientLPM - LPM based on steepest subgradient descent


  \subsubsection svmclassi_sec 支持向量机（SVM）
  \li CSVMLight - A variant of SVMlight using pr_loqo as its internal solver.
  \li CLibSVM - LibSVM modified to use shoguns kernel framework.
  \li CMPDSVM - Minimal Primal Dual SVM
  \li CGPBTSVM - Gradient Projection Technique SVM
  \li CWDSVMOcas - CSVMOcas based SVM using explicitly spanned WD-Kernel feature space
  \li CGMNPSVM - A true multiclass one vs. rest SVM
  \li CGNPPSVM - SVM solver based on the generalized nearest point problem
  \li CMCSVM - An experimental multiclass SVM
  \li CLibSVMMultiClass - LibSVMs one vs. one multiclass SVM solver
  \li CLibSVMOneClass - LibSVMs one-class SVM


  \subsection distmachine_sec 距离学习机
  \li k-Nearest Neighbor - Standard k-NN




  \section regression_sec 回归
  \subsection 支持向量回归（SVR）
  \li CSVRLight - SVMLight based SVR
  \li CLibSVR - LIBSVM based SVR

  \subsection other_regress 其它
  \li CKRR - Kernel Ridge Regression



  \section distrib_sec 分布
  \li CHMM - Hidden Markov Models
  \li CHistogram - Histogram
  \li CLinearHMM - Markov chains (embedded in ``Linear'' HMMs)




  \section cluster_sec 聚类
  \li CHierarchical - Agglomerative hierarchical single linkage clustering.
  \li CKMeans - k-Means Clustering




  \section mkl_sec 多核函数学习（Multiple Kernel Learning）
  \li CMKLRegression for q-norm MKL with Regression
  \li CMKLOneClass for q-norm 1-class MKL
  \li CMKLClassification for q-norm 2-class MKL
  \li CGMNPMKL for 1-norm multi-class MKL




  \section kernels_sec 核函数
  \li CAUCKernel - To maximize AUC in SVM training (takes a kernel as input)
  \li CChi2Kernel - Chi^2 Kernel
  \li CCombinedKernel - Combined kernel to work with multiple kernels
  \li CCommUlongStringKernel - Spectrum Kernel with spectrums of up to 64bit
  \li CCommWordStringKernel - Spectrum kernel with spectrum of up to 16 bit
  \li CConstKernel - A ``kernel'' returning a constant
  \li CCustomKernel - A user supplied custom kernel
  \li CDiagKernel - A kernel with nonzero elements only on the diagonal
  \li CDistanceKernel - A transformation to transform distances into similarities
  \li CFixedDegreeStringKernel - A string kernel
  \li CGaussianKernel - The standard Gaussian kernel
  \li CGaussianShiftKernel - Gaussian kernel with shift (inspired by the Weighted Degree shift kernel
  \li CGaussianShortRealKernel - Gaussian Kernel on 32bit Floats
  \li CHistogramWordStringKernel - A TOP kernel on Sequences
  \li CLinearByteKernel - Linear Kernel on Bytes
  \li CLinearKernel - Linear Kernel
  \li CLinearStringKernel - Linear Kernel on Strings
  \li CLinearWordKernel - Linear Kernel on Words
  \li CLocalAlignmentStringKernel - The local alignment kernel
  \li CLocalityImprovedStringKernel - The locality improved kernel
  \li CMatchWordStringKernel - Another String kernel
  \li COligoStringKernel - The oligo string kernel
  \li CPolyKernel - the polynomial kernel
  \li CPolyMatchStringKernel - polynomial kernel on strings
  \li CPolyMatchWordStringKernel - polynomial kernel on strings
  \li CPyramidChi2 - pyramid chi2 kernel (from image analysis)
  \li CRegulatoryModulesStringKernel - regulatory modules string kernel
  \li CSalzbergWordStringKernel - salzberg features based string kernel
  \li CSigmoidKernel - Tanh sigmoidal kernel
  \li CSimpleLocalityImprovedStringKernel - A variant of the locality improved kernel
  \li CSparseGaussianKernel - Gaussian Kernel on sparse features
  \li CSparseLinearKernel - Linear Kernel on sparse features
  \li CSparsePolyKernel - Polynomial Kernel on sparse features
  \li CTensorProductPairKernel - The Tensor Product Pair Kernel (TPPK)
  \li CWeightedCommWordStringKernel - A weighted (or blended) spectrum kernel
  \li CWeightedDegreePositionStringKernel - Weighted Degree kernel with shift
  \li CWeightedDegreeStringKernel - Weighted Degree string kernel




  \subsection kernel_normalizer 核函数标准化
因为有些核函数对某些SVM来说数值不稳定，它们需要先作一些标准化。

  \li CSqrtDiagKernelNormalizer - divide kernel by square root of product of diagonal
  \li CAvgDiagKernelNormalizer - divide by average diagonal value
  \li CFirstElementKernelNormalizer - divide by first kernel element k(0,0)
  \li CIdentityKernelNormalizer - no normalization
  \li CDiceKernelNormalizer - normalization inspired by the dice coefficient
  \li CRidgeKernelNormalizer - adds a ridge on the kernel diagonal
  \li CTanimotoKernelNormalizer - tanimoto coefficient inspired normalizer
  \li CVarianceKernelNormalizer - normalize vectors in feature space to norm 1




  \section dist_sec 距离
距离用于度量两个对象之间的矩离。它们可以用在CDistanceMachine对象中，如CKNN。
下面是已实现的矩离表示

  \li CBrayCurtisDistance - Bray curtis distance
  \li CCanberraMetric - Canberra metric
  \li CChebyshewMetric - Chebyshew metric
  \li CChiSquareDistance - Chi^2 distance
  \li CCosineDistance - Cosine distance
  \li CEuclidianDistance - Euclidian Distance
  \li CGeodesicMetric - Geodesic metric
  \li CHammingWordDistance - Hammin distance
  \li CJensenMetric - Jensen metric
  \li CManhattanMetric - Manhatten metric
  \li CMinkowskiMetric - Minkowski metric
  \li CTanimotoDistance - Tanimoto distance




  \section eval_sec 评价
  \subsection perf_sec 性能度量
  性能度量用于评价预测质量，在shogun中CPerformanceMeasures实现。下面是已实现的
  性能度量
  \li  Receiver Operating Curve (ROC)
  \li  Area under the ROC curve (auROC)
  \li  Area over the ROC curve (aoROC)
  \li  Precision Recall Curve (PRC)
  \li  Area under the PRC (auPRC)
  \li  Area over the PRC (aoPRC)
  \li  Detection Error Tradeoff (DET)
  \li  Area under the DET (auDET)
  \li  Area over the DET (aoDET)
  \li  Cross Correlation coefficient (CC)
  \li  Weighted Relative Accuracy (WRAcc)
  \li  Balanced Error (BAL)
  \li  F-Measure
  \li  Accuracy
  \li  Error

*/