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JMLR
2012
13 years 9 months ago
Age-Layered Expectation Maximization for Parameter Learning in Bayesian Networks
The expectation maximization (EM) algorithm is a popular algorithm for parameter estimation in models with hidden variables. However, the algorithm has several non-trivial limitat...
Avneesh Singh Saluja, Priya Krishnan Sundararajan,...
ICML
2004
IEEE
16 years 7 months ago
Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning
Reminder systems support people with impaired prospective memory and/or executive function, by providing them with reminders of their functional daily activities. We integrate tem...
Matthew R. Rudary, Satinder P. Singh, Martha E. Po...
GECCO
2007
Springer
186views Optimization» more  GECCO 2007»
16 years 21 days ago
Cascaded generic XCS to learn about reminding preferences
We are developing an adaptive reminding system, which learns when and how to present notifications. In this paper, we focus on our XCS-based model, composed of two cascaded sets ...
Nadine Richard, Samuel Tardieu, Seiji Yamada
IMECS
2007
15 years 8 months ago
Using Background Knowledge for Graph Based Learning: A Case Study in Chemoinformatics
The benefit of incorporating background knowledge in the learning process has been successfully demonstrated in numerous applications of ILP methods. Nevertheless the effect of inc...
Thashmee Karunaratne, Henrik Boström
CVPR
2001
IEEE
16 years 8 months ago
Learning Representative Local Features for Face Detection
This paper describes a face detection approach via learning local features. The key idea is that local features, being manifested by a collection of pixels in a local region, are ...
Xiangrong Chen, Lie Gu, Stan Z. Li, HongJiang Zhan...