CS229 Lecture notes Andrew Ng Part VI Regularization and model

First Page		Document Content
Date: 2007-12-11 16:41:19 Estimation theory Model selection Information theory Maximum likelihood Supervised learning Feature selection Kullback–Leibler divergence Regularization Mutual information Statistics Statistical theory Machine learning		CS229 Lecture notes Andrew Ng Part VI Regularization and model Add to Reading List Source URL: see.stanford.edu Download Document from Source Website File Size: 86,63 KB Share Document on Facebook

	Abstraction Selection in Model-Based Reinforcement Learning Nan Jiang Alex Kulesza Satinder Singh Computer Science & Engineering, University of Michigan DocID: 1vnBa - View Document
	Overview Introduction Parameter Estimation Model Selection Structure Discovery DocID: 1v46f - View Document
	Machine Learning manuscript No. (will be inserted by the editor) Model Selection in Reinforcement Learning Amir-massoud Farahmand1 , Csaba Szepesv´ ari1 DocID: 1uLfC - View Document
	A Philosophical Analysis of Bayesian Model Selection for Inequality Constrained Models Jan-Willem Romeijn1 and Rens van de Schoot2 1 2 DocID: 1uKWH - View Document
	Conditional Density Estimation by Penalized Likelihood Model Selection and Applications S. X. Cohen (IPANEMA / Synchrotron Soleil) and E. Le Pennec (SELECT / Inria Saclay - Île de France and Université Paris Sud) July DocID: 1uGIO - View Document