Titlebook: Machine Learning; Discriminative and G Tony Jebara Book 2004 Springer Science+Business Media New York 2004 Extension.computer science.learn

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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

BATE

Conclusion,raints. It spanned many important generative models allowing us to learn their parameters discriminatively. Other extensions were feasible beyond binary classification and an important iterative formulation for latent variables also emerged. MED thus provided a principled fusion of discriminative an

修飾

Book 2004derstandable to a larger portion of the public. This gives the more practical-minded engineer, student and the industrial public an easy-access and more sensible road map into the world of machine learning. ..Machine Learning: Discriminative and Generative. is designed for an audience composed of re

多產(chǎn)子

繁殖

right-atrium

持續(xù)

Meager

CON

Introduction,he question: is there a powerful connection between generative and discriminative learning that combines the complementary strengths of the two approaches? In this text, we undertake the challenge of building such a bridge and explicate a common formalism that spans both schools of thought.

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