Response modeling with support vector machines
Support Vector Machine (SVM) employs Structural Risk Minimization (SRM) principle to generalize better than conventional machine learning methods employing the traditional Empirical Risk Minimization (ERM) principle. When applying SVM to response modeling in direct marketing, however, one has to deal with the practical difficulties: large training data, class imbalance and scoring from binary SVM output. For the first difficulty, we propose a way to alleviate or solve it through a novel informative sampling. For the latter two difficulties, we provide guidelines within SVM framework so that one can readily use the paper as a quick reference for SVM response modeling: use of different costs for different classes and use of distance to decision boundary, respectively. This paper also provides various evaluation measures for response models in terms of accuracies, lift chart analysis, and computational efficiency.

title: Fast Pattern Selection for Support Vector Classifiers
abstract: SVMs tend to take a very long time to train with a large data set. If "redundant" patterns are identified and deleted in pre-processing, the training time could be reduced significantly. We propose a k-nearest neighbors(k-NN) based pattern selection method. The method tries to select the patterns that are near the decision boundary and that are correctly labeled. The simulations over synthetic data sets showed promising results: (1) By converting a non-separable problem to a separable one, the search for an optimal error tolerance parameter became unnecessary. (2) SVM training time decreased by two orders of magnitude without any loss of accuracy. (3) The redundant SVs were substantially reduced.

title: Neighborhood PropertyBased Pattern Selection for Support Vector Machines
abstract: The support vector machine (SVM) has been spotlighted in the machine learning community because of its theoretical soundness and practical performance. When applied to a large data set, however, it requires a large memory and a long time for training. To cope with the practical difficulty, we propose a pattern selection algorithm based on neighborhood properties. The idea is to select only the patterns that are likely to be located near the decision boundary. Those patterns are expected to be more informative than the randomly selected patterns. The experimental results provide promising evidence that it is possible to successfully employ the proposed algorithm ahead of SVM training.

title: Fast Pattern Selection Algorithm for Support Vector Classifiers: Time Complexity Analysis
abstract: Training SVM requires large memory and long cpu time when the pattern set is large. To alleviate the computational burden in SVM training, we propose a fast preprocessing algorithm which selects only the patterns near the decision boundary. The time complexity of the proposed algorithm is much smaller than that of the naive M 2 algorithm

title: Automated ordering of fingerprinted clones
abstract: A considerable amount of human intervention is currently required to produce high-quality fingerprint-based physical maps for genomic studies.



An algorithm has been developed and implemented to automatically order fingerprinted clones within contigs. The resulting software, named CORAL (Clone ORdering ALgorithm), has been tested on maps that have previously been manually edited and on maps derived from in silico simulations. The fingerprint map and DNA sequence of the human genome has provided an additional test to CORAL. Measurements suggest that CORAL performs significantly better than the software currently used by most laboratories to order fingerprinted clones at throughputs far exceeding those that can be achieved manually.

title: The study of quick download mechanism in an infrastructural wireless environment
abstract: Frequent handoff consumes a lot of system capability and signalling resources in a mobile system. In this paper, we present download mechanisms in mobile systems or broadcast systems to support data users to receive the whole packet in time before moving out of the coverage area. Three download strategies: Quick Download using Multi-channel (QDM) scheme, Prior Download using Single channel (PDS) scheme and the Quit Download (QUD) scheme are proposed in this study where the efficiencies of the three strategies are compared with the one without particular treatment. Some significant estimations such as the percentage of handoff data users having completed service before leaving the cell, the mean waiting time of non-handoff data users, and the blocking probability of data users are analysed and compared. The numerical results show that, the QDM strategy is most efficient in reducing the handoff rate, the QUD strategy can assure the waiting time of non-handoff data users and the PDS strategy can keep the handoff rate constant at light and heavy voice load. Furthermore, various number of guard channels for data users are evaluated in terms of data and voice performance based on various download strategies.

title: An efficient color representation for image retrieval
abstract: A compact color descriptor and an efficient indexing method for this descriptor are presented. The target application is similarity retrieval in large image databases using color. Colors in a given region are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the region. A similarity measure similar to the quadratic color histogram distance measure is defined for this descriptor. The representative colors can be indexed in the three-dimensional (3-D) color space thus avoiding the high-dimensional indexing problems associated with the traditional color histogram. For similarity retrieval, each representative color in the query image or region is used independently to find regions containing that color. The matches from all of the query colors are then combined to obtain the final retrievals. An efficient indexing scheme for fast retrieval is presented. Experimental results show that this compact descriptor is effective and compares favorably with the traditional color histogram in terms of overall computational complexity.

title: Color image segmentation
abstract: In this work, a new approach to fully automatic color image segmentation, called JSEG, is presented. First, colors in the image are quantized to several representing classes that can be used to differentiate regions in the image. Then, image pixel colors are replaced by their corresponding color class labels, thus forming a class-map of the image. A criterion for "good" segmentation using this class-map is proposed. Applying the criterion to local windows in the class-map results in the "J-image", in which high and low values correspond to possible region boundaries and region centers, respectively. A region growing method is then used to segment the image based on the multi-scale J-images. Experiments show that JSEG provides good segmentation results on a variety of images.

title: The study of dynamic multi-channel scheme with channel de-allocation in wireless networks
abstract: This paper proposes an analytical model to demonstrate the benefit of data service in wireless networks using dynamic multi-channel scheme with channel de-allocation (DMC-w-DA). Buffers are provided for voice calls to reduce the voice blocking probability caused by data packets contention. All free channels are allocated to data users dynamically. For those data users using more than one channel, channels would be de-allocated for new requests, voice or data. Handoff calls are given priority to be queued in the front of the buffer instead of providing guard channels to reduce their dropping probability To compensate the data packet loss probability in data, guard channels are provided for data traffic. Based on a given handoff dwell time, we analyze the dropping probability of handoff call. The probability distribution of receiving service within the handoff dwell time is obtained for each buffered handoff call and based on this result, an appropriate amount of handoff call allowed in buffer is obtained. Numerical results show that the dynamic multi-channel scheme with possible de-allocation, compared with the dynamic channel scheme without channel de-allocation, fixed channel allocation and single channel allocation scheme, can enhance data traffic performance significantly in terms of the mean service time and the packet loss probability. A system providing buffer to voice traffic and giving priority to queued handoff calls can indeed decrease new call blocking probability and handoff call dropping probability.