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: 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: 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: 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: 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: 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: An Accurate Worst Case Timing Analysis for RISC Processors
abstract: An accurate and safe estimation of a task's worst case execution time (WCET) is crucial for reasoning about the timing properties of real-time systems. In RISC processors, the execution time of a program construct (e.g., a statement) is affected by various factors such as cache hits/misses and pipeline hazards, and these factors impose serious problems in analyzing the WCETs of tasks. To analyze the timing effects of RISC's pipelined execution and cache memory, we propose extensions to the original timing schema where the timing information associated with each program construct is a simple time-bound. In our approach, associated with each program construct is worst case timing abstraction, (WCTA), which contains detailed timing information of every execution path that might be the worst case execution path of the program construct. This extension leads to a revised timing schema that is similar to the original timing schema except that concatenation and pruning operations on WCTAs are newly defined to replace the add and max operations on time-bounds in the original timing schema. Our revised timing schema accurately accounts for the timing effects of pipelined execution and cache memory not only within but also across program constructs. This paper also reports on preliminary results of WCET analysis for a RISC processor. Our results show that tight WCET bounds (within a maximum of about 30% overestimation) can be obtained by using the revised timing schema approach.

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: On the error probability of binary and M-ary signals in Nakagami-m fading channels
abstract: In this letter, we present new closed-form formulas for the exact average symbol-error rate (SER) of binary and M-ary signals over Nakagami-m fading channels with arbitrary fading index m. Using the well-known moment generating function-based analysis approach, we express the average SER in terms of the higher transcendental functions such as the Gauss hypergeometric function, Appell hypergeometric function, or Lauricella function. The results are generally applicable to arbitrary real-valued m. Furthermore, with the aid of reduction formulas of hypergeometric functions, we show previously published results for Rayleigh fading (m=1) as special cases of our expressions.

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: 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.