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ICML
2005
IEEE
16 years 5 months ago
Active learning for Hidden Markov Models: objective functions and algorithms
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by cl...
Brigham Anderson, Andrew Moore
GLVLSI
1996
IEEE
145views VLSI» more  GLVLSI 1996»
15 years 8 months ago
Boolean Function Representation Using Parallel-Access Diagrams
Inthispaperweintroduceanondeterministiccounterpart to Reduced, Ordered Binary Decision Diagrams for the representation and manipulation of logic functions. ROBDDs are conceptually...
Valeria Bertacco, Maurizio Damiani
BCS
2008
15 years 5 months ago
A Customisable Multiprocessor for Application-Optimised Inductive Logic Programming
This paper describes a customisable processor designed to accelerate execution of inductive logic programming, targeting advanced field-programmable gate array (FPGA) technology. ...
Andreas Fidjeland, Wayne Luk, Stephen Muggleton
112
Voted
COLT
2004
Springer
15 years 9 months ago
Concentration Bounds for Unigrams Language Model
Abstract. We show several PAC-style concentration bounds for learning unigrams language model. One interesting quantity is the probability of all words appearing exactly k times in...
Evgeny Drukh, Yishay Mansour
IDEAL
2007
Springer
15 years 10 months ago
Discriminating Microbial Species Using Protein Sequence Properties and Machine Learning
Abstract. Much work has been done to identify species-specific proteins in sequenced genomes and hence to determine their function. We assumed that such proteins have specific ph...
Ali Al-Shahib, David Gilbert, Rainer Breitling