Classes | |
class | AdaBoost |
AdaBoost (adaptive boosting). More... | |
class | AdaBoost_ECOC |
AdaBoost.ECC with exponential cost and Hamming distance. More... | |
class | Aggregating |
An abstract class for aggregating. More... | |
class | Bagging |
Bagging (boostrap aggregating). More... | |
class | Boosting |
Boosting generates a linear combination of hypotheses. More... | |
struct | _boost_gd |
class | Cascade |
Aggregate hypotheses in a cascade (sequential) way. More... | |
class | CGBoost |
CGBoost (Conjugate Gradient Boosting). More... | |
struct | _boost_cg |
class | DataFeeder |
Feed (random splitted) training and testing data. More... | |
class | dataset |
Class template for storing, retrieving, and manipulating a vector of input-output style data. More... | |
class | FeedForwardNN |
class | LearnModel |
A unified interface for learning models. More... | |
class | LPBoost |
LPBoost (Linear-Programming Boosting). More... | |
struct | MgnCost |
Cost proxy used in MgnBoost. More... | |
class | MgnBoost |
MgnBoost (margin maximizing boosting). More... | |
struct | _mgn_gd |
class | MultiClass_ECOC |
Multiclass classification using error-correcting output code. More... | |
class | NNLayer |
A layer in a neural network. More... | |
struct | _search |
Interface used in iterative optimization algorithms. More... | |
struct | _gradient_descent |
Gradient descent. More... | |
struct | _gd_weightdecay |
Gradient descent with weight decay. More... | |
struct | _gd_momentum |
Gradient descent with momentum. More... | |
struct | _gd_adaptive |
struct | _line_search |
struct | _conjugate_gradient |
class | Perceptron |
Perceptron models a type of artificial neural network that consists of only one neuron, invented by Frank Rosenblatt in 1957. More... | |
class | Pulse |
Multi-transition pulse functions (step functions). More... | |
class | Stump |
Decision stump. More... | |
class | SVM |
Namespaces | |
namespace | cost |
namespace | details |
namespace | kernel |
namespace | op |
Operators used in optimization. | |
Typedefs | |
typedef std::vector< std::vector< REAL > > | WMAT |
typedef std::vector< DataWgt > | JointWgt |
typedef const_shared_ptr< JointWgt > | pJointWgt |
typedef var_shared_ptr< LearnModel > | pLearnModel |
typedef std::vector< REAL > | Input |
typedef std::vector< REAL > | Output |
typedef dataset< Input, Output > | DataSet |
typedef std::vector< REAL > | DataWgt |
typedef const_shared_ptr< DataSet > | pDataSet |
typedef const_shared_ptr< DataWgt > | pDataWgt |
typedef std::vector< int > | ECOC_VECTOR |
typedef std::vector< ECOC_VECTOR > | ECOC_TABLE |
typedef std::map< REAL, REAL >::iterator | MI |
typedef svm_node * | p_svm_node |
Enumerations | |
enum | ECOC_TYPE { ONE_VS_ONE, ONE_VS_ALL } |
Functions | |
DataSet * | load_data (std::istream &, UINT, UINT, UINT) |
Load a data set from a stream. | |
DataSet * | load_data (std::istream &is, UINT n) |
template<class SEARCH> | |
void | iterative_optimize (SEARCH s) |
Main search routine. | |
bool | ldivide (RMAT &A, const RVEC &b, RVEC &x) |
void | update_wgt (RVEC &wgt, const RVEC &dir, const RMAT &X, const RVEC &y) |
void | dset_extract (const pDataSet &ptd, RMAT &X, RVEC &y) |
void | dset_mult_wgt (const pDataWgt &ptw, RVEC &y) |
UINT | randcdf (REAL r, const RVEC &cdf) |
bool | ldivide (std::vector< std::vector< REAL > > &A, const std::vector< REAL > &b, std::vector< REAL > &x) |
p_svm_node | fill_svm_node (const Input &x, struct svm_node *pool) |
Variables | |
const kernel::RBF | _svm_ker (0.5) |
The idea is to separate the learning model and optimization techniques.
Using vectorop.h for default vector operation
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Definition at line 26 of file learnmodel.h. |
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Definition at line 27 of file learnmodel.h. |
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Definition at line 21 of file multiclass_ecoc.h. |
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Definition at line 20 of file multiclass_ecoc.h. |
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Definition at line 23 of file learnmodel.h. |
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Definition at line 16 of file adaboost_ecoc.h. |
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Definition at line 24 of file learnmodel.h. |
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Definition at line 28 of file learnmodel.h. |
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Definition at line 29 of file learnmodel.h. |
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Definition at line 17 of file adaboost_ecoc.h. |
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Definition at line 17 of file aggregating.h. |
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Definition at line 27 of file adaboost_ecoc.cpp. |
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Definition at line 22 of file multiclass_ecoc.h. |
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Definition at line 179 of file perceptron.cpp. Referenced by Perceptron::train(). |
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Definition at line 189 of file perceptron.cpp. |
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Definition at line 92 of file svm.cpp. Referenced by SVM::kernel(), SVM::operator()(), and SVM::signed_margin(). |
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Main search routine.
Definition at line 74 of file optimize.h. Referenced by MgnBoost::train(), FeedForwardNN::train(), CGBoost::train_gd(), and Boosting::train_gd(). |
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Solve inv(A) * b, when A is symmetric and positive-definite. Actually we only need the upper triangular part of A. |
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Definition at line 120 of file perceptron.cpp. References Cholesky_decomp(), and Cholesky_linsol(). |
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An easier-to-use version, where the output dimension is fixed at 1, and the input dimension is auto-detected. This version requires that each row of stream is should be a sample. Definition at line 46 of file learnmodel.cpp. References dataset::append(), and dataset::size(). |
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Load a data set from a stream. Each sample consists of first the input and then the output. Numbers are separated by spaces.
Definition at line 37 of file learnmodel.cpp. Referenced by DataFeeder::DataFeeder(). |
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Definition at line 324 of file perceptron.cpp. |
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Update the weight wgt along the direction dir. If necessary, the whole wgt will be negated. Definition at line 131 of file perceptron.cpp. References DOTPROD. |
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