Structural Hidden Markov Models Using a Relation of Equivalence: Application to Automotive Designs
Data Mining and Knowledge Discovery
Structural hidden Markov models: An application to handwritten numeral recognition
Intelligent Data Analysis
Structural hidden Markov models based on stochastic context-free grammars
Control and Intelligent Systems
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We have introduced in this paper the concept of structural hidden Markov models (SHMM's). This new paradigm adds the syntactical (or structural) component to the traditional HMM's. SHMM's introduce relationships between the visible observations of a sequence. These observations are related because they are viewed as evidences of a same conclusion in a rule of inference. We have applied this novel concept to predict customer's preferences for automotive designs. SHMMhas outperformed both the k-nearest neighbors and the neural network classifiers with an additional 12% increase in accuracy.