207099457
Improved sparse least-squares support vector machine classifiers
The least-squares support vector machines (LS-SVM) can be obtained by solving a simpler optimization problem than that in standard support vector machines (SVM). Its shortcoming is the loss of sparseness and this usually results in slow testing speed. Several pruning methods have been proposed. It is found that these methods can be further improved for classification problems. In this paper a different reduced training set is selected to re-train LS-SVM. Then a new procedure is proposed to obtain the sparseness. The performance of the proposed method is compared with other typical ones and the results indicate that it is more effective.
43211087
Simplify Decision Function of Reduced Support Vector Machines
Reduced Support Vector Machines (RSVM) was proposed as the alternate of standard support vector machines (SVM) in order to resolve the difficulty in the learning of nonlinear SVM for large data set problems. RSVM preselects a subset as support vectors and solves a smaller optimization problem, and it performs well with remarkable efficiency on training of SVM for large problem. All the training points of the subset will be support vectors, and more training points are selected into this subset results in high possibility to obtain RSVM with better generalization ability. So we first obtain the RSVM with more support vectors, and selects out training examples near classification hyper plane. Then only these training examples are used as training set to obtain a standard SVM with less support vector than that of RSVM. Computational results show that standard SVMs on the basis of RSVM have much less support vectors and perform equal generalization ability to that of RSVM.
15388288
Performance of Linear-Space Search Algorithms
Abstract Search algorithms that use space linear in the search depth are widely employed in practice to solve difficult problems optimally, such as planning and scheduling. In this paper, we study the average-case performance of linear-space search algorithms, including depth-first branch-and-bound (DFBnB), iterative-deepening (ID), and recursive best-first search (RBFS). To facilitate our analyses, we use a random tree T(b,d) that has mean branching factor b , depth d , and node costs that are the sum of the costs of the edges from the root to the nodes. We prove that the expected number of nodes expanded by DFBnB on a random tree is no more than bd times the expected number of nodes expanded by best-first search (BFS) on the same tree, which usually requires space that is exponential in depth d . We also show that DFBnB is asymptotically optimal when BFS runs in exponential time, and ID and RBFS are asymptotically optimal when the edge costs of T ( b , d ) are integers. If bp 0 is the expected number of children of a node whose costs are the same as that of their parent, then the expected number of nodes expanded by these three linear-space algorithms is exponential when bp o d 4 ) when bp 0 = 1, and at most quadratic when bp 0 > 1. In addition, we study the heuristic branching factor of T ( b , d ) and the effective branching factor of BFS, DFBnB, ID, and RBFS on T ( b , d ). Furthermore, we use our analytic results to explain a surprising anomaly in the performance of these algorithms, and to predict the existence of a complexity transition in the Asymmetric Traveling Salesman Problem.
6016662
Generalized optimal thresholding for biometric key generation using face images
In this paper, we study biometric key generation using face images. Given a face image, a set of biometric features are extracted. Each feature is compared with a threshold to generate a key-bit. By cascading the key-bits from all the features, we obtain one bio-key that can be used for security applications. The performance of a biometric key generation system, determined by the chosen thresholds, can be evaluated according to reliability and security. A generalized optimal thresholding method is proposed in this paper to improve the reliability by minimizing the authentication error rate and the security by maximizing the entropy of the generated key.
18427300
Exploring Functional Unit Design Space of VLIW Processors for Optimizing Both Performance and Energy Consumption
The number of functional units can have significant impact on both the performance and energy consumption of VLIW processors. This paper uses a design exploration approach to find optimal integer functional unit configurations for achieving the best EDP (energy delay product) results for different media applications. Our experimental results quantitatively indicate that the optimal number of integer functional units should match the instruction level parallelism that can be extracted from the applications to balance both performance and energy optimally for VLIW processors.
12895493
Improvement of HITS-based algorithms on web documents
In this paper, we present two ways to improve the precision of HITS-based algorithms on Web documents. First, by analyzing the limitations of current HITS-based algorithms, we propose a new weighted HITS-based method that assigns appropriate weights to in-links of root documents. Then, we combine content analysis with HITS-based algorithms and study the effects of four representative relevance scoring methods, VSM, Okapi, TLS, and CDR, using a set of broad topic queries. Our experimental results show that our weighted HITS-based method performs significantly better than Bharat's improved HITS algorithm. When we combine our weighted HITS-based method or Bharat's HITS algorithm with any of the four relevance scoring methods, the combined methods are only marginally better than our weighted HITS-based method. Between the four relevance-scoring methods, there is no significant quality difference when they are combined with a HITS-based algorithm.
28880809
A feature-oriented approach to modeling requirements dependencies
There are many researches on requirements dependencies. However, most of them limit their views to the requirements phase of software development, few focus on the roles of requirements dependencies in the solution space of software. This paper presents a feature-oriented approach to modeling requirements dependencies. A feature is a set of tight-related requirements from user/customer-views. The feature-orientation provides a modular way to organize requirements and a proper granularity to analyze requirements dependencies. In this approach, we care about not only static feature dependencies (i. e. refinements and constraints), but also feature dependencies at the specification level (namely influences) and, furthermore, dynamic feature dependencies (namely interactions). Moreover, we also explore the underlying connections between these four kinds of feature dependency. By this way, this approach gives a more complete view of how requirements dependencies influence the whole process of software development.