> The Spider Objects

>       Core 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
>         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.)

>       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 SVM
>         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 gradient descent 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]

>       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

>       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