An incremental model selection algorithm based on cross-validation for finding the architecture of a Hidden Markov model on hand gesture data sets
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Dosyalar
Tarih
2009-12-13
Yazarlar
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Dergi ISSN
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Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In a multi-parameter learning problem, besides choosing the architecture of the learner, there is the problem of finding the optimal parameters to get maximum performance. When the number of parameters to be tuned increases, it becomes infeasible to try all the parameter sets, hence we need an automatic mechanism to find the optimum parameter setting using computationally feasible algorithms. In this paper, we define the problem of optimizing the architecture of a Hidden Markov Model (HMM) as a state space search and propose the MSUMO (Model Selection Using Multiple Operators) framework that incrementally modifies the structure and checks for improvement using cross-validation. There are five variants that use forward/backward search, single/multiple operators, and depth-first/breadth-first search. On four hand gesture data sets, we compare the performance of MSUMO with the optimal parameter set found by exhaustive search in terms of expected error and computational complexity.
Açıklama
Anahtar Kelimeler
Hidden Markov model, Model selection, Cross-validation, Automatic mechanisms, Data sets, Exhaustive search, Feasible algorithms, Hand gesture, Incremental models, Multiparameters, Multiple operator, Optimal parameter, Optimum parameters, Parameter set, State space search, Computational complexity, Mathematical operators, Optimization, Learning systems, Hidden Markov models, Machine learning, Computer architecture, Machine learning algorithms, Data engineering, Graphical models, Bayesian methods, Application software, State-space methods, Learning (artificial intelligence), Tree searching, Incremental model selection algorithm, Hand gesture data sets, Multiparameter learning problem, State space search, Forward/backward search, Single/multiple operators, Depth-first/breadth-first search
Kaynak
8th International Conference on Machine Learning and Applications
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Ulaş, A. & Yıldız, O. T. (2009). An incremental model selection algorithm based on cross-validation for finding the architecture of a hidden markov model on hand gesture data sets. Paper presented at the 8th International Conference on Machine Learning and Applications, 170-177. doi:10.1109/ICMLA.2009.91