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» Approximate Expectation Maximization
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AI
2009
Springer
15 years 6 months ago
Context Dependent Movie Recommendations Using a Hierarchical Bayesian Model
Abstract. We use a hierarchical Bayesian approach to model user preferences in different contexts or settings. Unlike many previous recommenders, our approach is content-based. We...
Daniel Pomerantz, Gregory Dudek
130
Voted
ICTAI
2010
IEEE
14 years 8 months ago
Unsupervised Greedy Learning of Finite Mixture Models
This work deals with a new technique for the estimation of the parameters and number of components in a finite mixture model. The learning procedure is performed by means of a expe...
Nicola Greggio, Alexandre Bernardino, Cecilia Lasc...
STOC
2002
ACM
118views Algorithms» more  STOC 2002»
15 years 11 months ago
On the advantage over a random assignment
: We initiate the study of a new measure of approximation. This measure compares the performance of an approximation algorithm to the random assignment algorithm. This is a useful ...
Johan Håstad, Srinivasan Venkatesh
ENTCS
2006
118views more  ENTCS 2006»
14 years 11 months ago
Domain Theoretic Solutions of Initial Value Problems for Unbounded Vector Fields
This paper extends the domain theoretic method for solving initial value problems, described in [8], to unbounded vector fields. Based on a sequence of approximations of the vecto...
Abbas Edalat, Dirk Pattinson
CDC
2010
IEEE
160views Control Systems» more  CDC 2010»
14 years 6 months ago
Adaptive bases for Q-learning
Abstract-- We consider reinforcement learning, and in particular, the Q-learning algorithm in large state and action spaces. In order to cope with the size of the spaces, a functio...
Dotan Di Castro, Shie Mannor