SPIDER The Spider Objects

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
reg_jkm - Structure Output Learning using Joint Kernel Method
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
Objects marked with - are available in the extra package.