Active Bibliography

4 Part 1: Overview of the Probably Approximately Correct (PAC) Learning Framework – David Haussler - 1995
18 How Well do Bayes Methods Work for On-Line Prediction of {±1} values? – D. Haussler, A. Barron - 1992
18 Data Filtering and Distribution Modeling Algorithms for Machine Learning – Yoav Freund, Manfred K. Warmuth, David Haussler, David P. Helmbold - 1993
!()+, -./01 23456 – Department Of Computer, David P. Dobkin, Dimitrios Gunopulos, Wolfgang Maass, Technische Universitaet Graz - 1995
134 SCHAPIRE: Adaptive game playing using multiplicative weights – Yoav Freund, Robert E. Schapire - 1999
136 Universal prediction – Neri Merhav, Senior Member, Meir Feder, Senior Member - 1998
12 Learning by Canonical Smooth Estimation, Part I: Simultaneous Estimation – Kevin L. Buescher, P. R. Kumar - 1996
18 Probabilistic Analysis of Learning in Artificial Neural Networks: The PAC Model and its Variants – Martin Anthony - 1997
Exploring Applications of Learning Theory to Pattern Matching and Dynamic Adjustment of TCP Acknowledgment Delays – Stephen Donald Scott - 1998
Dynamic Adjustment of TCP Acknowledgment Delays – n.n.
40 Probably Approximately Correct Learning – David Haussler - 1990
13 Universal switching linear least squares prediction – Suleyman S. Kozat, Andrew C. Singer - 2006
75 Sequential Prediction of Individual Sequences Under General Loss Functions – David Haussler, Jyrki Kivinen, Manfred K. Warmuth - 1998
Lectures on Prediction of Individual Sequences – Gábor Lugosi - 2001
4 On the Complexity of Function Learning – Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger - 1994
3 Knowledge acquisition in statistical learning theory – Shai Fine - 1999
5 Learning with Limited Visibility – Eli Dichterman - 1998
32 Learning with Restricted Focus of Attention – Shai Ben-David, Eli Dichterman - 1997
Computational Learning Theory – Sally A. Goldman