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IJCAI
2007
15 years 8 months ago
Incremental Construction of Structured Hidden Markov Models
This paper presents an algorithm for inferring a Structured Hidden Markov Model (S-HMM) from a set of sequences. The S-HMMs are a sub-class of the Hierarchical Hidden Markov Model...
Ugo Galassi, Attilio Giordana, Lorenza Saitta
NIPS
2007
15 years 8 months ago
A Spectral Regularization Framework for Multi-Task Structure Learning
Learning the common structure shared by a set of supervised tasks is an important practical and theoretical problem. Knowledge of this structure may lead to better generalization ...
Andreas Argyriou, Charles A. Micchelli, Massimilia...
DMIN
2006
160views Data Mining» more  DMIN 2006»
15 years 8 months ago
MF-tree: Extracting and Clustering the Structural Features from Music Object in MusicXML
In the music information retrieval field, the most important topic is to extract the feature which represents the content from the music objects. The content feature is useful for ...
Yu-Chih Shen, Jia-Lien Hsu, Shuk-Chun Chung
EUSFLAT
2003
122views Fuzzy Logic» more  EUSFLAT 2003»
15 years 8 months ago
Structure recognition on sequences with a neuro-fuzzy-system
We present a formal description of a neurofuzzy system capable of aligning two sequences recognizing their internal structure. The alignment is done on two levels: grouping of the...
Klaus Dalinghaus, Tillman Weyde
IICAI
2003
15 years 8 months ago
Performance Analysis of an Acyclic Genetic approach to Learn Bayesian Network Structure
Abstract. We introduce a new genetic algorithm approach for learning a Bayesian network structure from data. Our method is capable of learning over all node orderings and structure...
Pankaj B. Gupta, Vicki H. Allan