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» Discriminative Mixed-Membership Models
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ESEM
2007
ACM
15 years 10 months ago
The Effects of Over and Under Sampling on Fault-prone Module Detection
The goal of this paper is to improve the prediction performance of fault-prone module prediction models (fault-proneness models) by employing over/under sampling methods, which ar...
Yasutaka Kamei, Akito Monden, Shinsuke Matsumoto, ...
RE
2010
Springer
15 years 1 months ago
Feature Unweaving: Refactoring Software Requirements Specifications into Software Product Lines
The design of the variability of a software product line is crucial to its success and evolution. Meaningful variable features need to be elicited, analyzed, documented and validat...
Reinhard Stoiber, Samuel Fricker, Michael Jehle, M...
ECCV
2008
Springer
16 years 8 months ago
Multiple Component Learning for Object Detection
Abstract. Object detection is one of the key problems in computer vision. In the last decade, discriminative learning approaches have proven effective in detecting rigid objects, a...
Boris Babenko, Pietro Perona, Piotr Dollár,...
ECCV
2004
Springer
16 years 8 months ago
Dimensionality Reduction by Canonical Contextual Correlation Projections
A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed. It is derived from classica...
Marco Loog, Bram van Ginneken, Robert P. W. Duin
ICCV
2005
IEEE
15 years 12 months ago
Object Categorization by Learned Universal Visual Dictionary
This paper presents a new algorithm for the automatic recognition of object classes from images (categorization). Compact and yet discriminative appearance-based object class mode...
John M. Winn, Antonio Criminisi, Thomas P. Minka