Fast Pattern Selection for Support Vector Classifiers
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: Observational Learning with Modular Networks
abstract: 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: Fast protein classification with multiple networks
abstract: Support vector machines (SVMs) have been successfully used to classify proteins into functional categories. Recently, to integrate multiple data sources, a semidefinite programming (SDP) based SVM method was introduced. In SDP/SVM, multiple kernel matrices corresponding to each of data sources are combined with weights obtained by solving an SDP. However, when trying to apply SDP/SVM to large problems, the computational cost can become prohibitive, since both converting the data to a kernel matrix for the SVM and solving the SDP are time and memory demanding. Another application-specific drawback arises when some of the data sources are protein networks. A common method of converting the network to a kernel matrix is the diffusion kernel method, which has time complexity of O(n(3)), and produces a dense matrix of size n x n.



We propose an efficient method of protein classification using multiple protein networks. Available protein networks, such as a physical interaction network or a metabolic network, can be directly incorporated. Vectorial data can also be incorporated after conversion into a network by means of neighbor point connection. Similar to the SDP/SVM method, the combination weights are obtained by convex optimization. Due to the sparsity of network edges, the computation time is nearly linear in the number of edges of the combined network. Additionally, the combination weights provide information useful for discarding noisy or irrelevant networks. Experiments on function prediction of 3588 yeast proteins show promising results: the computation time is enormously reduced, while the accuracy is still comparable to the SDP/SVM method.


AVAILABILITY
Software and data will be available on request.

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: Capacity of multiple-antenna fading channels: spatial fading correlation, double scattering, and keyhole
abstract: The capacity of multiple-input multiple-output (MIMO) wireless channels is limited by both the spatial fading correlation and rank deficiency of the channel. While spatial fading correlation reduces the diversity gains, rank deficiency due to double scattering or keyhole effects decreases the spatial multiplexing gains of multiple-antenna channels. In this paper, taking into account realistic propagation environments in the presence of spatial fading correlation, double scattering, and keyhole effects, we analyze the ergodic (or mean) MIMO capacity for an arbitrary finite number of transmit and receive antennas. We assume that the channel is unknown at the transmitter and perfectly known at the receiver so that equal power is allocated to each of the transmit antennas. Using some statistical properties of complex random matrices such as Gaussian matrices, Wishart (1928) matrices, and quadratic forms in the Gaussian matrix, we present a closed-form expression for the ergodic capacity of independent Rayleigh-fading MIMO channels and a tight upper bound for spatially correlated/double scattering MIMO channels. We also derive a closed-form capacity formula for keyhole MIMO channels. This analytic formula explicitly shows that the use of multiple antennas in keyhole channels only offers the diversity advantage, but provides no spatial multiplexing gains. Numerical results demonstrate the accuracy of our analytical expressions and the tightness of upper bounds.

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: New Disk Scheduling Algorithms for Reduced Rotational Latency
abstract: Disk arm scheduling algorithms have been studied for many years to increase disk I/O performance. Most of the disks by the early 1980’s are characterized as a linear seek time and their seek time is responsible for the most time of disk access. So, the existing disk scheduling algorithms have focused on the reduction of the average seek distance. Seek time has improved greatly and today’s disks usually have nonlinear seek time characteristics, whereas the rotation speed has been steady at the 3600 RPM. So, it is of value to reduce average rotational latency. In this paper we propose two disk scheduling algorithms, i.e. Shortest Rotational Latency First(SRLF) and Shortest Access Tie Fist(SATF) for reduced rotational latency. SRLF services first a request with the shortest expected rotational latency and SATF services first a request with the shortest expected access time. We analyze the expected access time of the request serviced first by each of SSTF and SRLF under the uniform cylinder access. We evaluated the response time of SSTF, SRLF, and SATF through the simulations under the uniform and localized cylinder access. The results of the analysis and simulations show that the algorithms proposed in this paper are more efficient than SSTF which is known for the fastest response time.

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.

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.