Abstract book (pdf) - ICPR 2010

ThCT2 Anadolu Auditorium
Classification - III Regular Session
Session chair: Tortorella, Francesco (Univ. degli Studi di Cassino)
15:40-16:00, Paper ThCT2.1
Nearest Archetype Hull Methods for Large-Scale Data Classification
Thurau, Christian, Fraunhofer IAIS
This paper introduces an efficient geometric approach for data classification that can build class models from large amounts
of high dimensional data. We determine a convex model of the data as the outcome of convex hull non-negative matrix
factorization, a large-scale variant of Archetypal Analysis. The resulting convex regions or archetype hulls give an optimal
(in a least squares sense) bounding of the data region and can be efficiently computed. We classify based on the minimum
distance to the closest archetype hull. The proposed method offers (i) an intuitive geometric interpretation, (ii) single as
well as multi-class classification, and (iii) handling of large amounts of high dimensional data. Experimental evaluation
on common benchmark data sets shows promising results.
16:00-16:20, Paper ThCT2.2
A Bound on the Performance of LDA in Randomly Projected Data Spaces
Durrant, Robert John, Univ. of Birmingham
Kaban, Ata, Univ. of Birmingham
We consider the problem of classification in nonadaptive dimensionality reduction. Specifically, we bound the increase in
classification error of Fisher’s Linear Discriminant classifier resulting from randomly projecting the high dimensional
data into a lower dimensional space and both learning the classifier and performing the classification in the projected
space. Our bound is reasonably tight, and unlike existing bounds on learning from randomly projected data, it becomes
tighter as the quantity of training data increases without requiring any sparsity structure from the data.
16:20-16:40, Paper ThCT2.3
Adaptive Incremental Learning with an Ensemble of Support Vector Machines
Kapp, Marcelo N., École de Tech. Supérieure - Univ. of Quebec
Sabourin, R., École de Tech. Supérieure
Maupin, Patrick, Defence Res. and Development Canada
The incremental updating of classifiers implies that their internal parameter values can vary according to incoming data.
As a result, in order to achieve high performance, incremental learner systems should not only consider the integration of
knowledge from new data, but also maintain an optimum set of parameters. In this paper, we propose an approach for performing
incremental learning in an adaptive fashion with an ensemble of support vector machines. The key idea is to track,
evolve, and combine optimum hypotheses over time, based on dynamic optimization processes and ensemble selection.
From experimental results, we demonstrate that the proposed strategy is promising, since it outperforms a single classifier
variant of the proposed approach and other classification methods often used for incremental learning.
16:40-17:00, Paper ThCT2.4
Margin Preserved Approximate Convex Hulls for Classification
Takahashi, Tetsuji, Hokkaido Univ.
Kudo, Mineichi, Hokkaido Univ.
The usage of convex hulls for classification is discussed with a practical algorithm, in which a sample is classified according
to the distances to convex hulls. Sometimes convex hulls of classes are too close to keep a large margin. In this paper, we
discuss a way to keep a margin larger than a specified value. To do this, we introduce a concept of ``expanded convex
hull’’ and confirm its effectiveness.
17:00-17:20, Paper ThCT2.5
Evolving Fuzzy Classifiers: Application to Incremental Learning of Handwritten Gesture Recognition Systems
Almaksour, Abdullah, IRISA/INSA de Rennes
Anquetil, Eric, IRISA/INSA
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