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AAAI
2008
15 years 8 days ago
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiya...
AAAI
2008
15 years 8 days ago
Constrained Classification on Structured Data
Most standard learning algorithms, such as Logistic Regression (LR) and the Support Vector Machine (SVM), are designed to deal with i.i.d. (independent and identically distributed...
Chi-Hoon Lee, Matthew R. G. Brown, Russell Greiner...
COLT
2008
Springer
14 years 11 months ago
On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms
Boosting algorithms build highly accurate prediction mechanisms from a collection of lowaccuracy predictors. To do so, they employ the notion of weak-learnability. The starting po...
Shai Shalev-Shwartz, Yoram Singer
EDUTAINMENT
2008
Springer
14 years 11 months ago
Reconstruction of Tree Crown Shape from Scanned Data
Reconstruction of a real tree from scattered scanned points is a new challenge in virtual reality. Although many progresses are made on main branch structures and overall shape of ...
Chao Zhu, Xiaopeng Zhang, Bao-Gang Hu, Marc Jaeger
BCSHCI
2007
14 years 11 months ago
Use study on a home video editing system
To help consumers dealing with their growing amount of home video, we have developed the Edit While Watching (EWW) system. It is designed to automatically create an edited version...
Hans Weda, Marco Campanella