_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 |
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) |
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 | |
Kernel | The operator() gives the inner-product in the transformed space |
LearnModel | A unified interface for learning models |
Linear | |
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 |
Perceptron | Perceptron models a type of artificial neural network that consists of only one neuron, invented by Frank Rosenblatt in 1957 |
Perceptron | |
Polynomial | |
Pulse | Multi-transition pulse functions (step functions) |
RBF | |
sigmoid | |
Sigmoid | |
Stump | |
Stump | Decision stump |
SVM | |
var_shared_ptr | Shared pointers (whose content can be changed) |