24771249
Parallel Approximate Matrix Factorization for Kernel Methods
The kernel methods play a pivotal role in machine learning algorithms. Unfortunately, working with the kernel methods must deal with an n times n kernel matrix, which is memory intensive. In this paper, we present a parallel, approximate matrix factorization algorithm, which loads only essential data to individual processors to enable parallel processing of data. Our method reduces space requirement for the kernel matrix from O(n2) to O(np/m), where n is the amount of data, p the reduced matrix dimension (p << n), and m the number of processors.
4409694
On route selection for interdomain traffic engineering
In this article we investigate a model of route selection for interdomain traffic engineering where routing to multiple destinations can be coordinated. We identify potential routing instability and inefficiency problems, and derive a set of practical guidelines to guarantee stability without global coordination. Using a realistic Internet topology, we show that route oscillations can happen even when a small number of ASes coordinate route selection for just a small number of destinations if the coordination does not follow our guidelines. Wc further extend our model so that ASes can adopt any route selection algorithms in a class of algorithms we call rational route selection algorithms; and the local ranking of routes of an AS can depend on ingress traffic patterns. We show that persistent route oscillations can happen in certain network settings even if the ASes strictly follow the constraints imposed by business considerations, and adopt any rational route selection algorithms.
9852672
NXG06-6: On the Efficient Implementation of Pipelined Heaps for Network Processing
Priority queues are often used in many network processing applications. Applications include sophisticated per-flow scheduling for providing advanced quality-of-service (QoS) guarantees, fast packet buffer memory management, and exact maintenance of statistics counters for real-time network measurements. In all these applications, the priority queues used must operate at very high-speeds, e.g. at 40 Gbps rates and beyond. One widely used data structure for implementing priority queues is the heap data structure. However, the logarithmic time complexity of heap operations is often too slow for increasingly fast line rates. To achieve constant time complexity, the pipelined heap structure has been proposed. In this paper, we describe new architecture techniques for the efficient implementation of pipelined heaps. In particular, we focus on aggressive memory management and pipelining techniques.
4427518
On the stability of rational, heterogeneous interdomain route selection
The recent discovery of instability caused by the interaction of local routing policies of multiple ASes has led to extensive research on the subject. However, previous studies analyze stability under a specific route selection algorithm. In this paper, instead of studying a specific route selection algorithm, we study a general class of route selection algorithms which we call rational route selection algorithms. We present a sufficient condition to guarantee routing convergence in a heterogeneous network where each AS runs any rational route selection algorithm. Applying our general results, we study the potential instability of a network where the preference of an AS depends on not only its egress routes to the destinations but also its inbound traffic patterns (i.e., the distribution of incoming traffic from its neighbors). We show that there exist networks which will have persistent route oscillations even when the ASes strictly follow the constraints imposed by business considerations, and adopt any rational route selection algorithms.
36787117
Parallelizing Support Vector Machines on Distributed Computers
Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel SVM algorithm (PSVM), which reduces memory use through performing a row-based, approximate matrix factorization, and which loads only essential data to each machine to perform parallel computation. Let n denote the number of training instances, p the reduced matrix dimension after factorization (p is significantly smaller than n), and m the number of machines. PSVM reduces the memory requirement from O(n2) to O(np/m), and improves computation time to O(np2/m). Empirical study shows PSVM to be effective. PSVM Open Source is available for download at http://code.google.com/p/psvm/.
10887794
Packet doppler: network monitoring using packet shift detection
Due to recent large-scale deployments of delay and loss-sensitive applications, there are increasingly stringent demands on the monitoring of service level agreement metrics. Although many end-to-end monitoring methods have been proposed, they are mainly based on active probing and thus inject measurement traffic into the network. In this paper, we propose a new scheme for monitoring service level agreement metrics, in particular, delay distribution. Our scheme is passive and therefore will not cause perturbation to real traffic. Using realistic delay and traffic demands, we show that our scheme achieves high accuracy and can detect burst events that will be missed by probing based methods.
15500084
Understanding the Prediction of Transmembrane Proteins by Support Vector Machine using Association Rule Mining
With the efforts to understand protein structure, many computational approaches have been made recently. Among them, the support vector machine (SVM) methods have been recently applied and showed successful performance compared with other machine learning schemes. However, despite the high performance, the SVM approaches suffer from the problem of understandability since it is a black-box model. To overcome this limitation, this study attempted to combine the SVM with the association rule based classifier which can present the meaningful explanation about the prediction. To perform this task, a new association rule based classifier (PCPAR) was devised based on the existing classifier, CPAR, to handle the sequential data. PCPAR creates the patterns by merging the generated rules and then classifies the sequential data based on the pattern match. The experimental result presents the following: with sequential data, the PCPAR scheme shows better performance with respect to the accuracy and the number of generated patterns than CPAR method whether applied alone or combined with SVM. The combined scheme of SVMPCPAR generates more compact patterns than the combined scheme of SVM with decision tree, SVM DT, with similar performance. These patterns are easily understandable and biologically meaningful
10887257
Foundations of SMS commerce success: lessons from SMS messaging and co-opetition
As a convenient and low-cost mobile communication technology, short messaging service (SMS) is experiencing very rapid growth. It is reported that 700 million mobile phone users worldwide sent 20 to 30 billion SMS messages every month in 2001. At the same time, SMS commerce applications have emerged to provide mobile users consumer-oriented services. The huge SMS messaging customer base serves as the potential SMS commerce customer base according to K. Kellett and A. Linde (2001) . However, the bloom in messaging business does not automatically lead to the success of general SMS commerce applications. In this paper, we analyzed SMS developments in three different countries to derive three success indicators for SMS messaging and relate them to the success of SMS commerce. Based on the proposed SMS application framework, we postulate four additional hypothesized success factors that could uniquely contribute to the success of SMS commerce. Our findings provide practical implications for promoting SMS commerce applications successfully.
30738286
The algorithms for dynamic scheduling
10224186
Bandwidth Selection and Reconstruction Quality in Point-Based Surfaces
We investigate the influence of bandwidth selection in the reconstruction quality of point-based surfaces. While the problem has received relatively little attention in the literature, we show that appropriate selection plays a significant role in the quality of reconstructed surfaces. We show how to compute optimal bandwidths for one class of moving least-squares surfaces by formulating the polynomial fitting step as a kernel regression problem for both noiseless and noisy data. In the context of Levin's projection, we also discuss the implications of the two-step projection for bandwidth selection. We show experimental comparisons of our method, which outperforms heuristically chosen functions and weights previously proposed. We also show the influence of bandwidth on the reconstruction quality of different formulations of point-based surfaces. We provide, to the best of our knowledge, the first quantitative comparisons between different MLS surface formulations and their optimal bandwidths. Using these experiments, we investigate the choice of effective bandwidths for these alternative formulations. We conclude with a discussion of how to effectively compare the different MLS formulations in the literature.
15663663
Kernel based articulated object tracking with scale adaptation and model update
Kernel based object tracking (KBOT) is one of the most popular and effective techniques for tracking task. However the constancy of the target model and unsound scale adaptation method are two main limitations. In this paper, we present a kernel based approach incorporated with scale estimation and target model update for articulated object tracking task. After predicating the object center with scale fixed KBOT, we extend scale selection theory to estimate the local optimal object scale. Once the object scale has been estimated, a kernel density estimation based strategy is developed to update the target model. Experimental results show that our approach is superior to traditional KBOT in the following two aspects: 1) it is less affected by the object scale change; 2) it is less prone to appearance variation.