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KDD
2012
ACM
187views Data Mining» more  KDD 2012»
12 years 6 days ago
Online learning to diversify from implicit feedback
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...
KDD
1997
ACM
109views Data Mining» more  KDD 1997»
14 years 1 months ago
Beyond Concise and Colorful: Learning Intelligible Rules
A variety of techniques from statistics, signal processing, pattern recognition, machine learning, and neural networks have been proposed to understand data by discovering useful ...
Michael J. Pazzani, Subramani Mani, William Rodman...
COGSCI
2010
99views more  COGSCI 2010»
13 years 10 months ago
Learning to Learn Causal Models
Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical B...
Charles Kemp, Noah D. Goodman, Joshua B. Tenenbaum
WSDM
2012
ACM
214views Data Mining» more  WSDM 2012»
12 years 5 months ago
Selecting actions for resource-bounded information extraction using reinforcement learning
Given a database with missing or uncertain content, our goal is to correct and fill the database by extracting specific information from a large corpus such as the Web, and to d...
Pallika H. Kanani, Andrew K. McCallum
CE
2004
88views more  CE 2004»
13 years 9 months ago
Beyond formal learning: Informal community eLearning
The goal of the study described in this paper was to gain an improved understanding of the social context of UK online centres and issues around the creation and exchange of knowl...
John Cook, Matt Smith