Abstract book (pdf) - ICPR 2010

We present a method of performing kernel space domain description of a dataset with incomplete entries without the need
for imputation, allowing kernel features of a class of data with missing features to be rigorously described. This addresses
the problem that absent data completion is usually required before kernel classifiers, such as support vector domain description
(SVDD), can be applied; equally, few existing techniques for incomplete data adequately address the issue of
kernel spaces. Our method, which we call instance-specific domain description (ISDD), uses a parametrisation framework
to compute minimal kernelised distances between data points with missing features through a series of optimisation runs,
allowing evaluation of the kernel distance while avoiding subjective completions of missing data. We compare results of
our method against those achieved by SVDD applied to an imputed dataset, using synthetic and experimental datasets
where feature absence has a non-trivial structure. We show that our methods can achieve tighter sphere bounds when applied
to linear and quadratic kernels.
13:30-16:30, Paper WeBCT8.12
Learning a Strategy with Neural Approximated Temporal-Difference Methods in English Draughts
Fausser, Stefan, Univ. of Ulm
Schwenker, Friedhelm, Univ. of Ulm
Having a large game-tree complexity and being EXPTIME-complete, English Draughts, recently weakly solved during
almost two decades, is still hard to learn for intelligent computer agents. In this paper we present a Temporal-Difference
method that is nonlinear neural approximated by a 4-layer multi-layer perceptron. We have built multiple English Draughts
playing agents, each starting with a randomly initialized strategy, which use this method during self-play to improve their
strategies. We show that the agents are learning by comparing their winning-quote relative to their parameters. Our best
agent wins versus the computer draughts programs Neuro Draughts, KCheckers and CheckerBoard with the easych engine
and looses to Chinook, GuiCheckers and CheckerBoard with the strong cake engine. Overall our best agent has reached
an amateur league level.
13:30-16:30, Paper WeBCT8.13
Learning the Kernel Combination for Object Categorization
Zhang, Deyuan, Harbin Inst. of Tech.
Wang, Xiaolong, Harbin Inst. of Tech.
Liu, Bingquan, Harbin Inst. of Tech.
Although Support Vector Machines(SVM) succeed in classifying several image databases using image descriptors proposed
in the literature, no single descriptor can be optimal for general object categorization. This paper describes a novel framework
to learn the optimal combination of kernels corresponding to multiple image descriptors before SVM training, leading
to solve a quadratic programming problem efficiently. Our framework takes into account the variation of kernel matrix
and imbalanced dataset, which are common in real world image categorization tasks. Experimental results on Graz-01
and Caltech-101 image databases show the effectiveness and robustness of our algorithm.
13:30-16:30, Paper WeBCT8.14
SemiCCA: Efficient Semi-Supervised Learning of Canonical Correlations
Kimura, Akisato, NTT Corp.
Kameoka, Hirokazu, NTT Corp.
Sugiyama, Masashi, Tokyo Inst. of Tech.
Nakano, Takuho, University of Tokyo
Maeda, Eisaku, Communication Science Lab.
Sakano, Hitoshi, NTT
Ishiguro, Katsuhiko, NTT
Canonical correlation analysis (CCA) is a powerful tool for analyzing multi-dimensional paired data. However, CCA tends
to perform poorly when the number of paired samples is limited, which is often the case in practice. To cope with this
problem, we propose a semi-supervised variant of CCA named “Semi CCA” that allows us to incorporate additional unpaired
samples for mitigating overfitting. The proposed method smoothly bridges the eigenvalue problems of CCA and
principal component analysis (PCA), and thus its solution can be computed efficiently just by solving a single (generalized)
eigenvalue problem as the original CCA. Preliminary experiments with artificially generated samples and PASCAL VOC
data sets demonstrate the effectiveness of the proposed method.
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