| _boost_cg | |
| _boost_gd | |
| _conjugate_gradient | |
| _gd_adaptive | |
| _gd_momentum | Gradient descent with momentum |
| _gd_weightdecay | Gradient descent with weight decay |
| _gradient_descent | Gradient descent |
| _line_search | |
| _mgn_gd | |
| _register_creator | |
| _search | Interface used in iterative optimization algorithms |
| _shared_ptr | |
| AdaBoost | AdaBoost (adaptive boosting) |
| AdaBoost_ECOC | AdaBoost.ECC with exponential cost and Hamming distance |
| AdaBoost_ERP | AdaBoost.ERP (AdaBoost.ECC with Re-Partitioning) |
| AdaCost | |
| Aggregating | An abstract class for aggregating |
| Bagging | Bagging (boostrap aggregating) |
| bisigmoid | |
| Boosting | Boosting generates a linear combination of hypotheses |
| Boosting::BoostWgt | Weight in gradient descent |
| Cascade | Aggregate hypotheses in a cascade (sequential) way |
| CGBoost | CGBoost (Conjugate Gradient Boosting) |
| const_shared_ptr | Shared pointers (whose content can not be changed) |
| CrossVal | A combination of cross-validation and model selection |
| DataFeeder | Feed (random splitted) training and testing data |
| DataFeeder::LINEAR_SCALE_PARAM | |
| dataset | Class template for storing, retrieving, and manipulating a vector of input-output style data |
| exponential | |
| FeedForwardNN | |
| HoldoutCrossVal | Randomized holdout cross-validation |
| Kernel | The operator() gives the inner-product in the transformed space |
| LearnModel | A unified interface for learning models |
| Linear | Linear kernel |
| logistic | |
| LPBoost | LPBoost (Linear-Programming Boosting) |
| MgnBoost | MgnBoost (margin maximizing boosting) |
| MgnCost | Cost proxy used in MgnBoost |
| MultiClass_ECOC | Multiclass classification using error-correcting output code |
| NNLayer | A layer in a neural network |
| Object | The root (ancestor) of all classes |
| Ordinal_BLE | Ordinal regression via binary learning on extended examples |
| Perceptron | Perceptron kernel |
| Perceptron | Perceptron models a type of artificial neural network that consists of only one neuron, invented by Frank Rosenblatt in 1957 |
| Polynomial | Polynomial kernel |
| Pulse | Multi-transition pulse functions (step functions) |
| RBF | RBF (Gausssian) kernel |
| sigmoid | |
| Sigmoid | Sigmoid kernel |
| Stump | Decision stump |
| Stump | Stump kernel |
| SVM | |
| var_shared_ptr | Shared pointers (whose content can be changed) |
| vFoldCrossVal | V-fold cross validation |
1.4.6