SPIDER The Spider Objects

Objects marked with - are available in the extra package.

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