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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

標簽： method statistic LEARNING Support

上傳時間： 2014-12-02

上傳用戶：zukfu
將軍——一種新的求解大規(guī)模問題的支持向量機程序（軟件）。A Large Scale Machine LEARNING Toolbox

將軍——一種新的求解大規(guī)模問題的支持向量機程序（軟件）。A Large Scale Machine LEARNING Toolbox

標簽： LEARNING Machine Toolbox Large

上傳時間： 2013-12-14

上傳用戶：zq70996813
Stroustrup s Guide To LEARNING C

Stroustrup s Guide To LEARNING C

標簽： Stroustrup LEARNING Guide To

上傳時間： 2016-01-17

上傳用戶：康郎
booster-tree a machine LEARNING method

booster-tree a machine LEARNING method

標簽： booster-tree LEARNING machine method

上傳時間： 2014-06-09

上傳用戶：龍飛艇
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.

標簽： meta-LEARNING classifiers combining Boosting

上傳時間： 2016-01-30

上傳用戶：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.

標簽： quantization k-nearest algorithm decision

上傳時間： 2016-02-07

上傳用戶：zhyiroy
LabVIEW7實用教程 LEARNING 全部資料完整版

LabVIEW7實用教程 LEARNING 全部資料完整版

標簽： LabVIEW7 LEARNING 實用教程

上傳時間： 2016-02-09

上傳用戶：gmh1314
LEARNING Multi-scale Block Local Binary Patterns for Face Recognition”

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

標簽： Multi-scale Recognition LEARNING Patterns

上傳時間： 2016-02-11

上傳用戶：cursor
Reinforcement LEARNING

Reinforcement LEARNING

標簽： Reinforcement LEARNING

上傳時間： 2016-02-13

上傳用戶：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

標簽： Second-Order Supervised Algorithm LEARNING

上傳時間： 2014-01-13

上傳用戶：zhuimenghuadie

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LEARNING

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

將軍——一種新的求解大規(guī)模問題的支持向量機程序（軟件）。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實用教程 LEARNING 全部資料完整版

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