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» Selecting Structural Patterns for Classification
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ICDAR
2009
IEEE
15 years 4 months ago
Learning Rich Hidden Markov Models in Document Analysis: Table Location
Hidden Markov Models (HMM) are probabilistic graphical models for interdependent classification. In this paper we experiment with different ways of combining the components of an ...
Ana Costa e Silva
ISM
2005
IEEE
107views Multimedia» more  ISM 2005»
15 years 3 months ago
Key Distributions as Musical Fingerprints for Similarity Assessment
— This paper presents a pitch-based approach for creating musical fingerprints for similarity assessment. An effective measure for musical similarity impacts music indexing and c...
Arpi Mardirossian, Elaine Chew
RE
2005
Springer
15 years 3 months ago
Model-Driven Visual Requirements Engineering
This paper describes the requirements handling process of a set of research projects, the ATHENA IP, and how this process is supported by modeling and visualizing requirement stru...
Helge Grenager Solheim, Frank M. Lillehagen, Sobah...
ICCTA
2007
IEEE
14 years 10 months ago
Digital Signal Types Identification Using a Hierarchical SVM-Based Classifier and Efficient Features
Automatic digital signal type identification (ADSTI) is an important topic for both military and civilian communication applications. Most of proposed techniques (identifiers) can...
Ataollah Ebrahimzadeh, Seyed Alireza Seyedin
JMLR
2000
134views more  JMLR 2000»
14 years 9 months ago
Learning with Mixtures of Trees
This paper describes the mixtures-of-trees model, a probabilistic model for discrete multidimensional domains. Mixtures-of-trees generalize the probabilistic trees of Chow and Liu...
Marina Meila, Michael I. Jordan