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3
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Part 1: Overview of the Probably Approximately Correct (PAC) Learning Framework
– David Haussler
- 1995
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17
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How Well do Bayes Methods Work for On-Line Prediction of {±1} values?
– D. Haussler, A. Barron
- 1992
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18
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Data Filtering and Distribution Modeling Algorithms for Machine Learning
– Yoav Freund, Manfred K. Warmuth, David Haussler, David P. Helmbold
- 1993
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!()+, -./01 23456
– Department Of Computer, David P. Dobkin, Dimitrios Gunopulos, Wolfgang Maass, Technische Universitaet Graz
- 1995
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106
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Adaptive Game Playing Using Multiplicative Weights
– Yoav Freund, Robert E. Schapire
|
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99
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Universal Prediction
– Neri Merhav, Meir Feder
- 1998
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Dynamic Adjustment of TCP Acknowledgment Delays
– Chapter Preliminaries In
- 105
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Exploring Applications of Learning Theory to Pattern Matching and Dynamic Adjustment of TCP Acknowledgment Delays
– Stephen Donald Scott
- 1998
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16
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Probabilistic Analysis of Learning in Artificial Neural Networks: The PAC Model and its Variants
– Martin Anthony
- 1994
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11
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Learning by Canonical Smooth Estimation, Part I: Simultaneous Estimation
– Kevin L. Buescher, P. R. Kumar
- 1996
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37
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Probably Approximately Correct Learning
– David Haussler
- 1990
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8
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Universal switching linear least squares prediction
– Suleyman S. Kozat, Andrew C. Singer
- 2006
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58
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Sequential Prediction of Individual Sequences Under General Loss Functions
– David Haussler, Jyrki Kivinen, Manfred K. Warmuth
- 1998
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Lectures on Prediction of Individual Sequences
– Gábor Lugosi
- 2001
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4
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On the Complexity of Function Learning
– Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger
- 1994
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2
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Knowledge acquisition in statistical learning theory
– Shai Fine
- 1999
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Toyota Technological Institute—Chicago
– Shai Shalev-shwartz
- 2008
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29
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Learning with Restricted Focus of Attention
– Shai Ben-David, Eli Dichterman
- 1997
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5
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Learning with Limited Visibility
– Eli Dichterman
- 1998
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