Spider Extra Algorithms

  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.
clust clust
feat_sel FEAT_SEL feat_sel object
mclass mclass
mod_sel mod_sel
optimizers optimizers