一 spider主页http://www.kyb.mpg.de/bs/people/spider/ （也可以在google上搜索spider matlab得到），关于它的介绍可以参考网址资料
二 使用时为matlab+spider+Weka；因为spider中的一些算法引用了Weka，比如j48
安装注意：
1 matlab7（R14）
6.5版本对java的支持不够，还没有开发javaclasspath等函数
??? Undefined function or variable 'javaclasspath'.??? Undefined function or variable 'javaaddclasspath'.
2 jre1.4.2
matlab7自带的是1.4.2；matlab6自带的是1.3.可以在D:\MATLAB7\sys\java\jre\win32下看到。如果装了matlab7，使用它自带的1.4.2就可以了，尤其不要使用1.6，因为1.6太新了，matlab还不支持。可以在Matlab下使用 version -java查看JVM版本。
如果你想使用1.5的话，C:\Program Files\Java\jre1.5.0_10；把jre1.5.0_10这个文件夹拷贝到D:\MATLAB7\sys\java\jre\win32下，然后增加环境变量MATLAB_JAVA：D:\MATLAB7\sys\java\jre\win32\jre1.5.0_10。这一步如果有问题的话，重启Matlab会给出错误提示。找不到什么什么文件...
3 Weka3.4.10
使用weka版本低一些即可，高的不行，因为高版本的weka可能是用高版本的jvm支持的。
我使用的组合是 matlab7（R14）+jre1.4.2（matlab7自带的，不需要任何设置）+Weka3.4.10
weka 下载：http://www.cs.waikato.ac.nz/ml/weka/
三 使用方法
1 下载spider，有core和extra两个压缩包，把他们解压到同一个文件夹spider下面，然后放到$matlabroot\toolbox下面
2下载weka3.4.10，找到weka.jar放到$matlabroot\java\jar下面
3 启动Matlab打开$matlabroot\toolbox\spider\use_spider.m运行
提示spider的一些信息和 WEKA support enabled!表示成功了。
然后可以使用 help spider命令查看信息，他的功能列出如附录，然后就可以训练了。
四 一个简单的例子
X=rand(50)-0.5; Y=sign(sum(X,2));dtrain=data(X,Y);%生成训练集，也可以使用load()从文件读取
model=train(svm,dtrain));%使用函数train（）训练模型
rtest=test(dtest,model);%使用训练好的模型对验证集dtest测试，返回测试结果
五 附录spider信息
最新spider Version 1.71 (24/7/2006)
Basic library objects. data - Storing input data and output results data_global - Implementation of data object that limits memory overhead algorithm - Generic algorithm object group - Groups sets of objects together (algorithms or data) loss - Evaluates loss functions get_mean - Takes mean loss over groups of algs chain - Builds chains of objects: output of one to input of another param - To train and test different hyperparameters of an object cv - Cross validation using objects given data kernel - Evaluates and caches kernel functions distance - Evaluates and caches distance functions Statistical Tests objects. wilcoxon - Wilcoxon test of statistical significance of results corrt_test - Corrected resampled t-test - for dependent trials Dataset objects. spiral - Spiral dataset generator. toy - Generator of dataset with only a few relevant features toy2d - Simple 2d Gaussian problem generator toyreg - Linear Regression with o outputs and n inputs Pre-Processing objects normalize - Simple normalization of data map - General user specified mapping function of data Density Estimation objects. parzen - Parzen's windows kernel density estimator indep - Density estimator which assumes feature independence bayes - Classifer based on density estimation for each class gauss - Normal distribution density estimator Pattern Recognition objects. svm - Support Vector Machine (svm) c45 - C4.5 for binary or multi-class knn - k-nearest neighbours platt - Conditional Probability estimation for margin classifiers mksvm - Multi-Kernel LP-SVM anorm - Minimize the a-norm in alpha space using kernels lgcz - Local and Global Consistent Learner bagging - Bagging Classifier adaboost - ADABoost method hmm - Hidden Markov Model loom - Leave One Out Machine l1 - Minimize l1 norm of w for a linear separator kde - Kernel Dependency Estimation: general input/output machine dualperceptron - Kernel Perceptron ord_reg_perceptron - Ordinal Regression Perceptron (Shen et al.) splitting_perceptron - Splitting Perceptron (Shen et al.) budget_perceptron - Sparse, online Pereceptron (Crammer et al.) randomforest - Random Forest Decision Trees WEKA-Required j48 - J48 Decision Trees for binary WEKA-Required Multi-Class and Multi-label objects. one_vs_rest - Voting method of one against the rest (also for multi-label) one_vs_one - Voting method of one against one mc_svm - Multi-class Support Vector Machine by J.Weston c45 - C4.5 for binary or multi-class knn - k-nearest neighbours Feature Selection objects. feat_sel - Generic object for feature selection + classifier r2w2_sel - SVM Bound-based feature selection rfe - Recursive Feature Elimination (also for the non-linear case) l0 - Dual zero-norm minimization (Weston, Elisseeff) fsv - Primal zero-norm based feature selection (Mangasarian) fisher - Fisher criterion feature selection mars - selection algorithm of Friedman (greedy selection) clustub - Multi-class feature selection using spectral clustering mutinf - Mutual Information for feature selection. Regression objects. svr - Support Vector Regression gproc - Gaussian Process Regression relvm_r - Relevance vector machine multi_rr - (possibly multi-dimensional) ridge regression mrs - Multivariate Regression via Stiefel Constraints knn - k-nearest neighbours multi_reg - meta method for independent multiple output regression kmp - kernel matching pursuit kpls - kernel partial least squares lms - least mean squared regression [now obselete due to multi_rr] rbfnet - Radial Basis Function Network (with moving centers) reptree - Reduced Error Pruning Tree WEKA-Required Model Selection objects. gridsel - select parameters from a grid of values r2w2_sel - Selecting SVM parameters by generalization bound bayessel - Bayessian parameter selection Unsupervised objects. one_class_svm - One class SVM kmeans - K means clustering kvq - Kernel Vector Quantization kpca - Kernel Principal Components Analysis ppca - Probabilistic Principal Component Analysis nmf - Non-negative Matrix factorization spectral - Spectral clustering mrank - Manifold ranking ppca - Probabilistic PCA Reduced Set and Pre-Image objects. pmg_mds - Calculate Pre-Images based on multi-dimensional scaling pmg_rr - Calculate Pre-Images based on learning and ridge regression rsc_burges - Bottom Up Reduced Set; calculates reduced set based on gradient descent rsc_fp - Bottom Up Reduced Set; calculates reduced set for rbf with fixed-point iteration schemes rsc_mds - Top Down Reduced Set; calculates reduced set with multi-dimensional scaling rsc_learn - Top Down Reduced Set; calculates reduced set with ridge regression rss_l1 - Reduced Set Selection via L1 penalization rss_l0 - Reduced Set Selection via L0 penalization rss_mp - Reduced Set Selection via matching pursuit
