No coin nor oath required. For personal study only.
BOOK REPORTS 287 cock). Scheduling straight-line code using reinforcement learning and rollouts (Amy McGovern and Eliot Moss). Bayesian modeling of facial similarity (Baback Moghaddam, Tony Jebara and Alex Pentland). Reinfocement learning for trading (John Moody and Matthew Saffell). Graphical models for recognizing human interactions (Nuria M. Oliver, Barbara Rosario and Alex Pentland). Independent component analysis of intracellular calcium spike data (Klaus Prank, Julia BSrger, Alexander yon zur Miihlen, Georg Brabant and Christof SchSfl). Applications of multi-resolution neural networks to mammography (Clay D. Spence and Paul Sajda). Robot docking using mixtures of Gaussians (Matthew M. Williamson, Roderick Murray-Smith and Volker Hansen). Using collective intelligence to route Internet traffic (David H. Wolpert, Kagan Tumer and Jeremy Frank).
IX. Control, navigation and planning. Robust, efficient, globally-optimized reinforcement learning with the parti-game algorithm (Mohammad A. A1-Ansari and Ronald J. Williams). Gradient descent for general reinforcement learning (Leemon Baird and Andrew W. Moore). Non-linear PI control inspired by biological control systems (Lyndon J. Brown, Gregory E. Gonye and James S. Schwaber). Optimizing admission control while ensuring quality of service in multimedia networks via reinforcement learning (Timothy X. Brown, Hui Tong and Satinder Singh). Viewing classifier systems as model free learning in POMDPs (Akira Hayashi and Nobuo Suematsu). Finite-sample convergence rates for Q-learning and indirect algorithms (Michael Kearns and Satinder Singh). Exploring unknown environments with real-time search or reinforcement learning (Sven Koenig). The effect of eligibility traces on finding optimal memoryless policies in partially observable Markov decision proceases (John Loch). Learning instance-independent value functions to enhance local search (Robert Moll, Andrew G. Barto, Theodore J. Perkins and Richard S. Sutton). Barycentric interpolators for continuous space and time reinforcement learning (Remi Munos and Andrew W. Moore). Risk sensitive reinforcement learning (Ralph Neuneier and Oliver Mihatsch). Coordinate transformation learning of hand position feedback controller by using change of position error norm (Eimei Oyama and Susumu Tachi). Learning macro-actions in reinforcement learning (Jette Randlov). Reinforcement learning based on on-line EM algorithm (Masa-aki Sato and Shin Ishii). A reinforcement learning algorithm in partially observable environments using short-term memory (Nobuo Suematsu and Akira Hayashi). Improved switching among temporally abstract actions (Richard S. Sutton, Satinder Singh, Doina Precup and Balaraman Ravindran). Experimental results on learning stochastic memoryless policies for partially observable Markov processes (John K. Williams and Satinder Singh). Index of authors. Keyword index.
๐ SIMILAR VOLUMES
Contents: Contributor list. Overview (Anthony J.G. Hey). I. Feynman's course on computation. 1. Feynman and computation (John J. Hopfield). 2. Neural networks and physical systems with emergent collective computational abilities (John J. Hopfield). 3. Feynman as a colleague (Carver A. Mead). 4. Cole
Contents: Foreword. Preface. I. Introduction. 1. Writing ASP components. 2. Setting up and maintaining the ASP development environment. 3. ASP components and COM. 4. ASP components and threads. 5. Components, transactions, and the Microsoft transaction server. 6. Overview of the Intrinsit (built-in)
Methods in Neuronal Modelinq: From Ions to Networks (Second edition). Edited by Christof Koch and Idan Segev. MIT Press, Cambridge, MA. (1998). 671 pages. $60.00. Contents: Series foreword. Preface. 1. Kinetic models of synaptic transmission (Alain Destexhe, Zachary F. Mainen and Terrence J. Sejnow