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PERVASIVE
2009
Springer
15 years 11 months ago
Methodologies for Continuous Cellular Tower Data Analysis
This paper presents novel methodologies for the analysis of continuous cellular tower data from 215 randomly sampled subjects in a major urban city. We demonstrate the potential of...
Nathan Eagle, John A. Quinn, Aaron Clauset
ALT
2003
Springer
15 years 8 months ago
Can Learning in the Limit Be Done Efficiently?
Abstract. Inductive inference can be considered as one of the fundamental paradigms of algorithmic learning theory. We survey results recently obtained and show their impact to pot...
Thomas Zeugmann
NIPS
1992
15 years 5 months ago
Explanation-Based Neural Network Learning for Robot Control
How can artificial neural nets generalize better from fewer examples? In order to generalize successfully, neural network learning methods typically require large training data se...
Tom M. Mitchell, Sebastian Thrun
COLING
2008
15 years 6 months ago
Homotopy-Based Semi-Supervised Hidden Markov Models for Sequence Labeling
This paper explores the use of the homotopy method for training a semi-supervised Hidden Markov Model (HMM) used for sequence labeling. We provide a novel polynomial-time algorith...
Gholamreza Haffari, Anoop Sarkar
NIPS
2007
15 years 6 months ago
Distributed Inference for Latent Dirichlet Allocation
We investigate the problem of learning a widely-used latent-variable model – the Latent Dirichlet Allocation (LDA) or “topic” model – using distributed computation, where ...
David Newman, Arthur Asuncion, Padhraic Smyth, Max...