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» Selection of Generative Models in Classification
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ICDM
2005
IEEE
185views Data Mining» more  ICDM 2005»
15 years 3 months ago
Semi-Supervised Mixture of Kernels via LPBoost Methods
We propose an algorithm to construct classification models with a mixture of kernels from labeled and unlabeled data. The derived classifier is a mixture of models, each based o...
Jinbo Bi, Glenn Fung, Murat Dundar, R. Bharat Rao
INTERSPEECH
2010
14 years 4 months ago
HMM-based text-to-articulatory-movement prediction and analysis of critical articulators
In this paper we present a method to predict the movement of a speaker's mouth from text input using hidden Markov models (HMM). We have used a corpus of human articulatory m...
Zhen-Hua Ling, Korin Richmond, Junichi Yamagishi
ICSE
2009
IEEE-ACM
15 years 10 months ago
Learning operational requirements from goal models
Goal-oriented methods have increasingly been recognised as an effective means for eliciting, elaborating, analysing and specifying software requirements. A key activity in these a...
Alessandra Russo, Dalal Alrajeh, Jeff Kramer, Seba...
AMOST
2005
ACM
15 years 3 months ago
Modeling requirements for combinatorial software testing
The combinatorial approach to software testing uses models to generate a minimal number of test inputs so that selected combinations of input values are covered. The most common c...
Christopher M. Lott, Ashish Jain, Siddhartha R. Da...
AMC
2006
79views more  AMC 2006»
14 years 10 months ago
VC-dimension and structural risk minimization for the analysis of nonlinear ecological models
The problem of distinguishing density-independent (DI) from density-dependent (DD) demographic time series is important for understanding the mechanisms that regulate populations ...
Giorgio Corani, Marino Gatto