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» A distributed machine learning framework
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115
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CORR
2011
Springer
192views Education» more  CORR 2011»
14 years 8 months ago
Distribution-Independent Evolvability of Linear Threshold Functions
Valiant’s (2007) model of evolvability models the evolutionary process of acquiring useful functionality as a restricted form of learning from random examples. Linear threshold ...
Vitaly Feldman
98
Voted
HICSS
2006
IEEE
163views Biometrics» more  HICSS 2006»
15 years 7 months ago
Learning Ranking vs. Modeling Relevance
The classical (ad hoc) document retrieval problem has been traditionally approached through ranking according to heuristically developed functions (such as tf.idf or bm25) or gene...
Dmitri Roussinov, Weiguo Fan
126
Voted
ECCV
2006
Springer
15 years 4 months ago
Learning Semantic Scene Models by Trajectory Analysis
Abstract. In this paper, we describe an unsupervised learning framework to segment a scene into semantic regions and to build semantic scene models from longterm observations of mo...
Xiaogang Wang, Kinh Tieu, Eric Grimson
102
Voted
ICML
1996
IEEE
16 years 1 months ago
On the Learnability of the Uncomputable
Within Valiant'smodel of learning as formalized by Kearns, we show that computable total predicates for two formallyuncomputable problems the classical Halting Problem, and t...
Richard H. Lathrop
FTDCS
1999
IEEE
15 years 5 months ago
Lessons Learned from Building a Web-Based Spatial Data Discovery Facility
This paper discusses the lessons learned and decisions made while designing and developing a web-based application for the National Spatial Information Framework (NSIF) of South A...
Ferdie Danzfuss, Judith Bishop