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NIPS
2000
15 years 2 months ago
Temporally Dependent Plasticity: An Information Theoretic Account
The paradigm of Hebbian learning has recently received a novel interpretation with the discovery of synaptic plasticity that depends on the relative timing of pre and post synapti...
Gal Chechik, Naftali Tishby
99
Voted
ATAL
2008
Springer
15 years 2 months ago
Graph Laplacian based transfer learning in reinforcement learning
The aim of transfer learning is to accelerate learning in related domains. In reinforcement learning, many different features such as a value function and a policy can be transfer...
Yi-Ting Tsao, Ke-Ting Xiao, Von-Wun Soo
79
Voted
KDD
1995
ACM
148views Data Mining» more  KDD 1995»
15 years 4 months ago
Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning
Knowledge discovery in databases has become an increasingly important research topic with the advent of wide area network computing. One of the crucial problems we study in this p...
Philip K. Chan, Salvatore J. Stolfo
107
Voted
EDM
2009
110views Data Mining» more  EDM 2009»
14 years 10 months ago
Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions o
A basic question of instructional interventions is how effective it is in promoting student learning. This paper presents a study to determine the relative efficacy of different in...
Mingyu Feng, Joseph Beck, Neil T. Heffernan
123
Voted
ICML
2010
IEEE
15 years 1 months ago
Boosting for Regression Transfer
The goal of transfer learning is to improve the learning of a new target concept given knowledge of related source concept(s). We introduce the first boosting-based algorithms for...
David Pardoe, Peter Stone