10102267
Parallel Randomized Support Vector Machine
A parallel support vector machine based on randomized sampling technique is proposed in this paper. We modeled a new LP-type problem so that it works for general linear-nonseparable SVM training problems unlike the previous work [2]. A unique priority based sampling mechanism is used so that we can prove an average convergence rate that is so far the fastest bounded convergence rate to the best of our knowledge. The numerical results on synthesized data and a real geometric database show that our algorithm has good scalability.
15882697
Coupling feature selection and machine learning methods for navigational query identification
It is important yet hard to identify navigational queries in Web search due to a lack of sufficient information in Web queries, which are typically very short. In this paper we study several machine learning methods, including naive Bayes model, maximum entropy model, support vector machine (SVM), and stochastic gradient boosting tree (SGBT), for navigational query identification in Web search. To boost the performance of these machine techniques, we exploit several feature selection methods and propose coupling feature selection with classification approaches to achieve the best performance. Different from most prior work that uses a small number of features, in this paper, we study the problem of identifying navigational queries with thousands of available features, extracted from major commercial search engine results, Web search user click data, query log, and the whole Web's relational content. A multi-level feature extraction system is constructed.Our results on real search data show that 1) Among all the features we tested, user click distribution features are the most important set of features for identifying navigational queries. 2) In order to achieve good performance, machine learning approaches have to be coupled with good feature selection methods. We find that gradient boosting tree, coupled with linear SVM feature selection is most effective. 3) With carefully coupled feature selection and classification approaches, navigational queries can be accurately identified with 88.1% F1 score, which is 33% error rate reduction compared to the best uncoupled system, and 40% error rate reduction compared to a well tuned system without feature selection.
14054770
A new approach for ground moving target indication in foliage environment
Ground moving target indication (GMTI) in foliage environment is a challenging problem, because the strong echoes from dense foliage environment and big attenuation of the radar microwave often make it difficult for traditional time frequency distributions to clearly and locally represent the Doppler frequency. This paper introduced a new approach to improve the performance of GMTI. This so-called Hilbert Huang Transform (HHT) method composes of empirical mode decomposition (EMD) and Hilbert transform technique. EMD is applied to extract target signal from interference and the associated Hilbert transform calculates the instantaneous Doppler frequency, representing a novel Hilbert spectrum in time frequency domain. Both simulative and experimental signals are used to validate the efficiency of HHT. This adaptive approach offers higher signal to disturbance ratio and higher resolution in the time frequency distribution.
18404090
Dynamic frequency scaling with buffer insertion for mixed workloads
This paper presents a method to reduce the energy of interactive systems for mixed workloads: multimedia applications that require constant output rates and sporadic jobs that need prompt responses. The authors' method divides multimedia programs into stages and inserts data buffers between them. Data buffering has three purposes: (1) to support constant output rates; (2) to allow frequency scaling for energy reduction; and (3) to shorten the response times of sporadic jobs. The authors construct frequency-assignment graphs. Each vertex represents the current state of the buffers and the frequencies of the processor. The authors develop an efficient graph-walk algorithm that assigns frequencies to reduce energy. The same method. can be applied to perform voltage scaling and the combination of frequency and voltage scaling. The authors' experimental results on a Strong-ARM-based computer show that four discrete frequencies are sufficient to achieve nearly maximum energy saving. The method reduces the power consumption of an MPEG program by 46%. The authors also demonstrate a case that shortens the response time of a sporadic job by 55%.
158009
Threshold optimization algorithm for weak signal in distributed-sensor fusion system
Distributed order statistics constant false alarm rate (OS-CFAR) detection techniques are important for nonstationary observation with varying weak narrowband random signals. However, it is very difficult to choose system parameters to obtain optimal threshold values at the fusion center because of the nonlinear property of distributed OS-CFAR detection system. This paper provides a novel solution based on an effective and flexible genetic algorithm. Using this approach, all system parameters are directly coded in decimal chromosomes and they can be optimized simultaneously. Furthermore, we also derive the more general model by implementing the K fusion rules. The simulation results show that applying the proposed approach one can achieve comparable performances with the reported methods and results.
1552056
Pama: a Fast String Matching Algorithm
String matching is a fundamental operation in computer science, and its performance has great impact on many applications including database query, text processing, DNA and protein sequence analysis. In this paper, we propose a fast string matching algorithm, PAMA (PAttern MAtching). The shift rule used by PAMA not only subsumes both the bad character rule and the good suffix rule employed by the well-known Boyer-Moore algorithm, but also employs an additional key observation to enable faster shifting during the string matching process. Theoretically, we prove that from the same alignment, the next shift of PAMA will be at least as much as that of the Boyer-Moore algorithm. Experimentally, we show that PAMA indeed significantly outperforms the original Boyer-Moore algorithm in almost all cases, and outperforms other Boyer-Moore variants such as Tuned-BM, Turbo-BM and Horspool for long patterns (length ≥ 128) or for small alphabets (size < 8).
7665820
Equivalence checking using abstract BDDs
We introduce a new equivalence checking method based on abstract BDDs (aBDDs). The basic idea is the following: given an abstraction function, aBDDs reduce the size of BDDs by merging nodes that have the same abstract value. An aBDD has bounded size and can be constructed without constructing the original BDD. We show that this method of equivalence checking is always sound. It is complete for an important class of arithmetic circuits that includes integer multiplication. We also suggest heuristics for findings suitable abstraction functions based on the structure of the circuit. The efficiency of this technique is illustrated by experiments on ISCAS'85 benchmark circuits.