Titlebook: Machine Learning; Modeling Data Locall Kaizhu Huang,Haiqin Yang,Michael Lyu Book 2008 Springer-Verlag Berlin Heidelberg 2008 ATSTC.Global l

CLOG

書目名稱Machine Learning影響因子(影響力)




書目名稱Machine Learning影響因子(影響力)學(xué)科排名




書目名稱Machine Learning網(wǎng)絡(luò)公開度




書目名稱Machine Learning網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning被引頻次




書目名稱Machine Learning被引頻次學(xué)科排名




書目名稱Machine Learning年度引用




書目名稱Machine Learning年度引用學(xué)科排名




書目名稱Machine Learning讀者反饋




書目名稱Machine Learning讀者反饋學(xué)科排名

Excitotoxin

秘密會(huì)議

AXIOM

的’

重疊

https://doi.org/10.1007/978-3-540-79452-3ATSTC; Global learning; Hybrid learning; Kernelization; Local learning; ZJUP; algorithms; computer science;

包庇

Learning Locally and Globally: Maxi-Min Margin Machine,The proposed MEMPM model obtains the decision hyperplane by using only global information, e.g. the mean and covariance matrices. However, although these moments can be more reliably obtained than estimating the distribution, they may still be inaccurate in many cases, e.g. when the data are very sparse.

含沙射影

Relinquish

先兆

1995-6819 resents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning."This theory not only connects previous machine learning methods, or serves a