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» Randomness, Stochasticity and Approximations
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GECCO
2007
Springer
162views Optimization» more  GECCO 2007»
15 years 10 months ago
Learning noise
In this paper we propose a genetic programming approach to learning stochastic models with unsymmetrical noise distributions. Most learning algorithms try to learn from noisy data...
Michael D. Schmidt, Hod Lipson
ICGI
2004
Springer
15 years 9 months ago
Learning Stochastic Finite Automata
Abstract. Stochastic deterministic finite automata have been introduced and are used in a variety of settings. We report here a number of results concerning the learnability of th...
Colin de la Higuera, José Oncina
122
Voted
ACL
1997
15 years 5 months ago
Finite State Transducers Approximating Hidden Markov Models
This paper describes the conversion of a Hidden Markov Model into a sequential transducer that closely approximates the behavior of the stochastic model. This transformation is es...
André Kempe
WINE
2007
Springer
124views Economy» more  WINE 2007»
15 years 10 months ago
Stochastic Models for Budget Optimization in Search-Based Advertising
Internet search companies sell advertisement slots based on users’ search queries via an auction. Advertisers have to solve a complex optimization problem of how to place bids o...
S. Muthukrishnan, Martin Pál, Zoya Svitkina
RTAS
2007
IEEE
15 years 10 months ago
Stochastic Metrics for Debugging the Timing Behaviour of Real-Time Systems
Stochastic analysis techniques for real-time systems model the execution time of tasks as random variables. These techniques constitute a very powerful tool to study the behaviour...
Joaquín Entrialgo, Javier García, Jo...