Observational Learning with Modular Networks
Observational learning algorithm is an ensemble algorithm where each network is initially trained with a bootstrapped data set and virtual data are generated from the ensemble for training. Here we propose a modular OLA approach where the original training set is partitioned into clusters and then each network is instead trained with one of the clusters. Networks are combined with different weighting factors now that are inversely proportional to the distance from the input vector to the cluster centers. Comparison with bagging and boosting shows that the proposed approach reduces generalization error with a smaller number of networks employed.

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: Invariance of neighborhood relation under input space to feature space mapping
abstract: If the training pattern set is large, it takes a large memory and a long time to train support vector machine (SVM). Recently, we proposed neighborhood property based pattern selection algorithm (NPPS) which selects only the patterns that are likely to be near the decision boundary ahead of SVM training Proc. of the 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Lecture Notes in Artificial Intelligence (LNAI 2637), Seoul, Korea, pp. 376-387. NPPS tries to identify those patterns that are likely to become support vectors in feature space. Preliminary reports show its effectiveness: SVM training time was reduced by two orders of magnitude with almost no loss in accuracy for various datasets. It has to be noted, however, that decision boundary of SVM and support vectors are all defined in feature space while NPPS described above operates in input space. If neighborhood relation in input space is not preserved in feature space, NPPS may not always be effective. In this paper, we show that the neighborhood relation is invariant under input to feature space mapping. The result assures that the patterns selected by NPPS in input space are likely to be located near decision boundary in feature space.

title: Graph Based Semi-supervised Learning with Sharper Edges
abstract: In many graph-based semi-supervised learning algorithms, edge weights are assumed to be fixed and determined by the data points' (often symmetric) relationships in input space, without considering directionality. However, relationships may be more informative in one direction (e.g. from labelled to unlabelled) than in the reverse direction, and some relationships (e.g. strong weights between oppositely labelled points) are unhelpful in either direction. Undesirable edges may reduce the amount of influence an informative point can propagate to its neighbours - the point and its outgoing edges have been blunted. We present an approach to sharpening in which weights are adjusted to meet an optimization criterion wherever they are directed towards labelled points. This principle can be applied to a wide variety of algorithms. In the current paper, we present one ad hoc solution satisfying the principle, in order to show that. it can improve performance on a number of publicly available benchmark data sets.

title: Response modeling with support vector machines
abstract: 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: 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 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: A packet scheduling scheme for broadband wireless networks with heterogeneous services
abstract: A frame-based packet scheduling scheme, the pinwheel scheduling (PWS) scheme, is proposed and investigated at the medium access control (MAC) layer in broadband wireless networks. The objective of the proposed scheduling scheme is to provide, respectively, low delay and low jitter for real-time traffic and short queue length for nonreal-time traffic. We have demonstrated in this paper that the proposed PWS scheme not only satisfies the packet-level QoS requirements but also has low implementation cost. As the number of constant bit rate (CBR) connections increases, variable bit rate (VBR) traffic would suffer a larger jitter, the PWS with modular strategy (PWS/MS) scheme is proposed to further enhance the PWS scheme. We also introduce two different slot-reuse strategies named real-time traffic first (RTF) and longest queue first (LQF), respectively, to improve the resource utilization. In order to demonstrate the performance of the PWS and PWS/MS schemes, comparisons are made with existing schemes such as the round-robin (RR) and weighted-round-robin (WRR) by using the OPNET software. Simulation results show that the proposed schemes are capable of maintaining the lowest delay and jitter for VBR and CBR traffic while not sacrificing the available bit rate (ABR) traffic.

title: Efficient Enforcement of Security Policies Based on Tracking of Mobile Users
abstract: Recent advances to mobile communication, Global Positioning System (GPS) and Radio Frequency Identification (RFID) technologies have propelled the growth of a number of mobile services. These require maintaining mobile object's location information and efficiently serving access requests on the past, present and future status of the moving objects. Moreover, these services raise a number of security and privacy challenges. To address this, security policies are specified to ensure controlled access to the mobile user's location and movement trajectories, their profile information, and stationary resources based on the mobile user's spatiotemporal information. Considering the basic authorization specification 〈subject, object, privilege 〉, in a mobile environment, a moving object can be a subject, an object, or both. Serving an access request requires to search for the desired moving objects that satisfy the query, as well as enforce the security policies. 
 
Often, enforcing security incurs overhead, and as a result may degrade the performance of a system. To alleviate this problem, recently Atluri and Guo have proposed an unified index structure, STPR-tree, to organize both the moving objects and authorizations specified over them. However, the STPR-tree is not capable supporting security policies based on tracking of mobile users. In this paper, we present an index structure, called SPPF-tree, which maintains past, present and future positions of the moving objects along with authorizations by employing partial persistent storage. We demonstrate how the SPPF-tree can be constructed and maintained, and provide algorithms to process two types of access requests, including moving object requests by stationary subjects such as locate and track, and stationary object requests by moving subjects.

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.