To install these algorithms please unzip into the same directory where spider is installed. The zip files contain the right target subdirectory. You can also download a snapshot of all extra algorithms here.

Core library objects
	wilcoxon	Wilcoxon object - significance test of results
	joint_kernel	Joint kernel object - for calculating inner products in joint feature spaces
	joint_data	Joint_data object
	corrt_test	corrected resampled t-test object - significance test of results
Unsupervised objects
	spectral	Spectral clustering
	ppca	Probabilistic Principal Components Analysis
	nmf	Non-negative Matrix Factorization object
	mrank	Manifold Ranking
Density Estimation objects
	indep	Feature selection by independent density estimation
Feature Selection objects
	r2w2_sel	Feature scaling/selection via SVMs and r^2/w^2 bound.
	nfe	Non-Linear Feature Elimination
	mutinf	Feature selection by mutual information
	mars	Greedy selection algorithm by J.Friedmann
	l0	L0 zero-norm minimization (Weston,Elisseeff dual method) object
	fsv	Primal zero-norm based feature selection by O.Mangasarian
	feat_sel	FEAT_SEL feat_sel object
	fisher	Select features based on Fisher/Correlation score
	clustub	Multi-class feature selection using spectral clustering
Multi-Class and Multi-label objects.
	mc_svm	Multi-class Support Vector Machine by J.Weston
Pattern Recognition objects.
	anorm	L1 Norm Minimization for kernel classifiers.
	budget_perceptron	Kernel Perceptron with Budget by Cramer et al.
	dualperceptron	Kernel Perceptron with optional margin.
	hmm	Hidden markov model - for learning time dependend structures
	j48	j48 Decision Tree [WEKA Required]
	randomforest	Random Forest Decision Tree by L.Breiman [WEKA Required]
	kde	Kernel dependency estimation - for general input-output relations
	l1	L1 norm for linear classifiers.
	lgcz	Semi-Supervised Learning by Zhou et al.
	loom	Leave One Out SVM by Weston et al.
	mksvm	Multi-Kernel LP-SVM following Weston (PhD-thesis, chapter 6)
	ord_reg_perceptron	Ordinal Regression Perceptron
	platt	Probabilistic outputs for SVM
	rank_perceptron	Ranking Perceptron by Collins et al.
	splitting_perceptron	Splitting Perceptron
Reduced Set and Pre-Image objects.
	pmg_mds	Pre-Image Technique using MDS by Kwok et al.
	rsc_burges	Reduced set construction by C.Burges
	rsc_fp	Reduced set construction using Fixpoint method by Schölkopf et al.
	rss_l1	Reduced set selection by L1 Penalizer.
Regression objects.
	relvm_r	The relevance vector machine for regression by M.Tipping
	reg_jkm	Structure Output Learning using Joint Kernel Method
	reptree	Reduced Error Pruning tree object
	rbfnet	Radial Basis Function Network object
	mde	Multiple output ridge regression object [Deprecated]
	kpls	Kernel Partial Least Squares by Rosipal et al.
Demos
	Contents
	clust	clust
	feat_sel	FEAT_SEL feat_sel object
	mclass	mclass
	mod_sel	mod_sel
	optimizers	optimizers