> The Spider Objects

>        Core library objects.

>         data         - Storing input data and output results
>         data_fast    - 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
>         wilcoxon     - Wilcoxon test of statistical significance of results

>        Pre-Processing objects

>         normalize    - Simple normalization of data
>         sigmoid      - Sigmoid normalization

>      Pattern Recognition objects.

>         svm          - Support Vector Machine (svm)
>         c45          - C4.5 for binary or multi-class
>         knn          - k-nearest neighbours
>         lda          - Linear Discriminant
>         platt        - Conditional Probability estimation for margin classifiers
>         anorm        - Minimize the a-norm in alpha space using kernels
>         loom         - Leave-one-out machine - kernel based classifier
>         stab         - Maximum Stability Classifier (via cv recursion)
>         kde          - Kernel Dependency Estimation: general input/output machine

>     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)
>         golub        - Golub's feature selection method using correlation scores
>         mars         - selection algorithm of Friedman (greedy selection)
>         corrub       - Unbalanced correlation score (used in the KDD Cup results)
>         clustub      - Multi-class feature selection using spectral clustering

>     Regression objects.

>         svr          - Support Vector Regression
>         stabreg      - Stability Regression
>         knn          - k-nearest neighbours
>         multi_reg    - meta method for independent multiple output regression
>         lms          - least mean squared regression
>         alr          - "adaptive (via leave-one-out) kernel" ridge regression

>     Model Selection objects.

>         gridsel      - select parameters from a grid of values

>     Unsupervised objects.

>         one_class_svm- One class SVM
>         kmeans       - K means clustering
>         hierarchical - Hierarchical clustering
>         kpca         - Kernel Principal Components Analysis
>         spectral     - Spectral clustering

>      Semi-Supervised objects.

>         trans_prob   - Transduction using iterative probability estimation
>         trans_marg   - Transduction by adding examples far from the margin

>       See also help on: demos, train, test.

> | Adding new objects.

>       See the tutorial section for guidelines on how to write a new object.
>       Add it by emailing Jason Weston.
>       Also send bug reports there.