BASIC OBJECTS
tl_algorithm	The base algorithm object
tl_cv	Cross-validation object
tl_distance	Object for calculating distances in feature spaces
tl_get_mean	Object for calculating means and standard errors of algorithms
tl_group	Grouping of objects
tl_kernel	Kernel object
tl_loss	Object for calculating the loss of a specified type
tl_mclass2mtask	Conversion of a multi-class problem to a multi-task problem
tl_mt_group	Grouping of multiple tasks
tl_mtask	Multitask object
tl_param	Parameters
tl_regret	Regret metric object
tl_spider	Wrapper for Spider objects
tl_task	Single task object
ALGORITHMS
tl_BBLasso	Blockwise Boosted Lasso TL algorithm of Obozinski et al
tl_AndoZhang	TL algorithm of Ando and Zhang
tl_FeatureSelection	Unsupervised feature selection over multiple tasks
tl_RainaNgKoller	Bayesian Transfer Learning via covariance matrix estimation
tl_hdp	Nonparametric Bayesian Transfer Learning (Hierarchical DPs)
tl_MetaPrior	MetaPrior Transfer Learning algorithm
tl_bugs	Interface to BUGS (Bayesian inference Using Gibbs Sampling)
DATA
tl_digits	Handwritten digits (UCI repository)
tl_grasp	Grasp point prediction data
tl_letters	Handwritten letters (MIT Spoken Language Systems Group)
tl_nips	NIPS conference articles
tl_rcv1	Reuters RCV1 data
tl_template_data	Template for creating data objects
tl_toy	Toy data
tl_usps	USPS dataset (reference)
ORIGINAL SPIDER OBJECTS	(list obtained here)
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