지난 3년간 프로시딩 목록을 훑으면서, 제목이 무슨 뜻인지 알아들을 수 있는 것만 골랐다. -_-; 집에 있는 학부 교과서부터 다 읽는게 우선인거 같긴 함...
ICML 2007
- Dimensionality Reduction and Generalization
- Multi-armed Bandit Problems with Dependent Arms
- Tracking Value Function Dynamics to Improve Reinforcement Learning with Piecewise Linear Function Approximation
ICML 2008
- Active Reinforcement Learning
- Fast Nearest Neighbor Retrieval for Bregman Divergences
- Space-indexed Dynamic Programming: Learning to Follow Trajectories
- An Analysis of Linear Models, Linear Value-Function Approximation, and Feature Selection for Reinforcement Learning
ICML 2009
- Boosting products of base classifiers decision stumps
- Learning Prediction Suffix Trees with Winnow
- Curriculum Learning
- Kernelized Value Function Approximation for Reinforcement Learning
- Model-Free Reinforcement Learning as Mixture Learning
Deep Belief Networks (레이어 많은 신경망) 란게 요즘은 열라 핫한거 같은데 어디 가서 공부해야 하나? -_-
