Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper WeBCT8.3
Nonlinear Combination of Multiple Kernels for Support Vector Machines
Li, Jinbo, East China Normal Univ.
Sun, Shiliang, East China Normal Univ.
Support vector machines (SVMs) are effective kernel methods to solve pattern recognition problems. Traditionally, they
adopt a single kernel chosen beforehand, which makes them lack flexibility. The recent multiple kernel learning (MKL)
overcomes this issue by optimizing over a linear combination of kernels. Despite its success, MKL neglects useful information
generated from the nonlinear interaction of different kernels. In this paper, we propose SVMs based on the nonlinear
combination of multiple kernels (NCMK) which surmounts the drawback of previous MKL by the potential to exploit
more information. We show that our method can be formulated as a semi-definite programming (SDP) problem then solved
by interior-point algorithms. Empirical studies on several data sets indicate that the presented approach is very effective.
13:30-16:30, Paper WeBCT8.4
Data Transformation of the Histogram Feature in Object Detection
Zhang, Rongguo, Chinese Acad. of Sciences
Xiao, Baihua, Chinese Acad. of Sciences
Wang, Chunheng, Chinese Acad. of Sciences
Detecting objects in images is very important for several application domains in computer vision. This paper presents an
experimental study on data transformation of the feature vector in object detection. We use the modified Pyramid of Histograms
of Orientation Gradients descriptor and the SVM classifier to form an object detection model. We apply a simple
transformation to the histogram features before training and testing. This transformation equals a small change in the
kernel function for Support Vector Machines. This change is much quicker than the kernel, but obtains better results. Experimental
evaluations on the UIUC Image Database and TU Darmstadt Database show that the transformed features perform
better than the raw features, and this transformation improves the linear separability of the histogram feature.
13:30-16:30, Paper WeBCT8.5
A New Learning Formulation for Kernel Classifier Design
Sato, Atsushi, NEC
This paper presents a new learning formulation for classifier design called ``General Loss Minimization.’’ The formulation
is based on Bayes decision theory which can handle various losses as well as prior probabilities. A learning method for
RBF kernel classifiers is derived based on the formulation. Experimental results reveal that the classification accuracy by
the proposed method is almost the same as or better than Support Vector Machine (SVM), while the number of obtained
reference vectors by the proposed method is much less than that of support vectors by SVM.
13:30-16:30, Paper WeBCT8.6
Variable Selection for Five-Minute Ahead Electricity Load Forecasting
Koprinska, Irena, Univ. of Sydney
Sood, Rohen, Univ. of Sydney
Agelidis, Vassilios, Univ. of Sydney
We use autocorrelation analysis to extract 6 nested feature sets of previous electricity loads for 5-minite ahead electricity
load forecasting. We evaluate their predictive power using Australian electricity data. Our results show that the most important
variables for accurate prediction are previous loads from the forecast day, 1, 2 and 7 days ago. By using also load
variables from 3 and 6 days ago, we achieved small further improvements. The 3 bigger feature sets (37-51 features) when
used with linear regression and support vector regression algorithms, were more accurate than the benchmarks. The overall
best prediction model in terms of accuracy and training time was linear regression using the set of 51 features.
13:30-16:30, Paper WeBCT8.7
Enhancing Web Page Classification via Local Co-Training
Du, Youtian, Xi’an Jiaotong Univ.
Guan, Xiaohong, Xi’an Jiaotong Univ., Tsinghua University
Cai, Zhongmin, Xi’an Jiaotong Univ.
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