LEARNING

Support Vector Machine is small sample method based on statistic LEARNING theory. It is a new method

Support Vector Machine is small sample method based on statistic LEARNING theory. It is a new method to deal with the highly nonlinear classification and regression problems .It can better deal with the small sample, nonlinear and

标签： method statistic LEARNING Support

上传时间： 2014-12-02

上传用户：zukfu
将军——一种新的求解大规模问题的支持向量机程序（软件）。A Large Scale Machine LEARNING Toolbox

将军——一种新的求解大规模问题的支持向量机程序（软件）。A Large Scale Machine LEARNING Toolbox

标签： LEARNING Machine Toolbox Large

上传时间： 2013-12-14

上传用户：zq70996813
Stroustrup s Guide To LEARNING C

Stroustrup s Guide To LEARNING C

标签： Stroustrup LEARNING Guide To

上传时间： 2016-01-17

上传用户：康郎
booster-tree a machine LEARNING method

booster-tree a machine LEARNING method

标签： booster-tree LEARNING machine method

上传时间： 2014-06-09

上传用户：龙飞艇
Boosting is a meta-LEARNING approach that aims at combining an ensemble of weak classifiers to form

Boosting is a meta-LEARNING approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.

标签： meta-LEARNING classifiers combining Boosting

上传时间： 2016-01-30

上传用户：songnanhua
主要是KNN（the k-nearest neighbor　algorithm ）,LVQ1（LEARNING vector quantization 1）, DSM（decision surface

主要是KNN（the k-nearest neighbor　algorithm ）,LVQ1（LEARNING vector quantization 1）, DSM（decision surface mapping）算法。 and a simple clustering algorithm.

标签： quantization k-nearest algorithm decision

上传时间： 2016-02-07

上传用户：zhyiroy
LabVIEW7实用教程 LEARNING 全部资料完整版

LabVIEW7实用教程 LEARNING 全部资料完整版

标签： LabVIEW7 LEARNING 实用教程

上传时间： 2016-02-09

上传用户：gmh1314
LEARNING Multi-scale Block Local Binary Patterns for Face Recognition”

LEARNING Multi-scale Block Local Binary Patterns for Face Recognition”

标签： Multi-scale Recognition LEARNING Patterns

上传时间： 2016-02-11

上传用户：cursor
Reinforcement LEARNING

Reinforcement LEARNING

标签： Reinforcement LEARNING

上传时间： 2016-02-13

上传用户：WMC_geophy
SpikeLM: A Second-Order Supervised LEARNING Algorithm for Training Spiking Neural Networks Introduc

SpikeLM: A Second-Order Supervised LEARNING Algorithm for Training Spiking Neural Networks Introduction Preliminaries SpikeLM algorithm Experimental validation Conclusions

标签： Second-Order Supervised Algorithm LEARNING

上传时间： 2014-01-13

上传用户：zhuimenghuadie

LEARNING

Support Vector Machine is small sample method based on statistic LEARNING theory. It is a new method

将军——一种新的求解大规模问题的支持向量机程序（软件）。A Large Scale Machine LEARNING Toolbox

Stroustrup s Guide To LEARNING C

booster-tree a machine LEARNING method

Boosting is a meta-LEARNING approach that aims at combining an ensemble of weak classifiers to form

主要是KNN（the k-nearest neighbor　algorithm ）,LVQ1（LEARNING vector quantization 1）, DSM（decision surface

LabVIEW7实用教程 LEARNING 全部资料完整版

LEARNING Multi-scale Block Local Binary Patterns for Face Recognition”

Reinforcement LEARNING

SpikeLM: A Second-Order Supervised LEARNING Algorithm for Training Spiking Neural Networks Introduc