22470543
A Weighted Technique in Heuristic Search
As shown in [1], we examine search as a statistic sampling process. Based on some statistical inference method the probability that a subtree in search tree contains the goal can be decided. Thus some weight is in tentionaly added to the evaluation function of those nodes which are unlikely in the solution path so that the search will concentrate on the most promising path. It results in a new weighted algorithm-WSA. 
 
Tn a uniform m-ary tree, we show that a goal can be found by WSA in the polynomial time, although the computational complexity of A (or A*) may be O(e) for searching the same space. Where N is the depth at which the goal is located.
7255987
Relationship between support vector set and kernel functions in SVM
Based on a constructive learning approach, covering algorithms, we investigate the relationship between support vector sets and kernel functions in support vector machines (SVM). An interesting result is obtained. That is, in the linearly non-separable case, any sample of a given sample setK can become a support vector under a certain kernel function. The result shows that when the sample setK is linearly non-separable, although the chosen kernel function satisfies Mercer’s condition its corresponding support vector set is not necessarily the subset ofK that plays a crucial role in classifyingK. For a given sample set, what is the subset that plays the crucial role in classification? In order to explore the problem, a new concept, boundary or boundary points, is defined and its properties are discussed. Given a sample setK, we show that the decision functions for classifying the boundary points ofK are the same as that for classifying theK itself. And the boundary points ofK only depend onK and the structure of the space at whichK is located and independent of the chosen approach for finding the boundary. Therefore, the boundary point set may become the subset ofK that plays a crucial role in classification. These results are of importance to understand the principle of the support vector machine (SVM) and to develop new learning algorithms.
9400771
Identification of biochemical networks by S-tree based genetic programming
MOTIVATION
Most previous approaches to model biochemical networks have focused either on the characterization of a network structure with a number of components or on the estimation of kinetic parameters of a network with a relatively small number of components. For system-level understanding, however, we should examine both the interactions among the components and the dynamic behaviors of the components. A key obstacle to this simultaneous identification of the structure and parameters is the lack of data compared with the relatively large number of parameters to be estimated. Hence, there are many plausible networks for the given data, but most of them are not likely to exist in the real system.


RESULTS
We propose a new representation named S-trees for both the structural and dynamical modeling of a biochemical network within a unified scheme. We further present S-tree based genetic programming to identify the structure of a biochemical network and to estimate the corresponding parameter values at the same time. While other evolutionary algorithms require additional techniques for sparse structure identification, our approach can automatically assemble the sparse primitives of a biochemical network in an efficient way. We evaluate our algorithm on the dynamic profiles of an artificial genetic network. In 20 trials for four settings, we obtain the true structure and their relative squared errors are <5% regardless of releasing constraints about structural sparseness. In addition, we confirm that the proposed algorithm is robust within +/-10% noise ratio. Furthermore, the proposed approach ensures a reasonable estimate of a real yeast fermentation pathway. The comparatively less important connections with non-zero parameters can be detected even though their orders are below 10(-2). To demonstrate the usefulness of the proposed algorithm for real experimental biological data, we provide an additional example on the transcriptional network of SOS response to DNA damage in Escherichia coli. We confirm that the proposed algorithm can successfully identify the true structure except only one relation.
2156360
Quantifying Path Exploration in the Internet
Previous measurement studies have shown the existence of path exploration and slow convergence in the global Internet routing system, and a number of protocol enhancements have been proposed to remedy the problem. However, existing measurements were conducted only over a small number of testing prefixes. There has been no systematic study to quantify the pervasiveness of Border Gateway Protocol (BGP) slow convergence in the operational Internet, nor any known effort to deploy any of the proposed solutions. In this paper, we present our measurement results that identify BGP slow convergence events across the entire global routing table. Our data shows that the severity of path exploration and slow convergence varies depending on where prefixes are originated and where the observations are made in the Internet routing hierarchy. In general, routers in tier-1 Internet service providers (ISPs) observe less path exploration, hence they experience shorter convergence delays than routers in edge ASs; prefixes originated from tier-1 ISPs also experience less path exploration than those originated from edge ASs. Furthermore, our data show that the convergence time of route fail-over events is similar to that of new route announcements and is significantly shorter than that of route failures. This observation is contrary to the widely held view from previous experiments but confirms our earlier analytical results. Our effort also led to the development of a path-preference inference method based on the path usage time, which can be used by future studies of BGP dynamics.
1777247
Web-page classification through summarization
Web-page classification is much more difficult than pure-text classification due to a large variety of noisy information embedded in Web pages. In this paper, we propose a new Web-page classification algorithm based on Web summarization for improving the accuracy. We first give empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web-page classification algorithms. We then propose a new Web summarization-based classification algorithm and evaluate it along with several other state-of-the-art text summarization algorithms on the LookSmart Web directory. Experimental results show that our proposed summarization-based classification algorithm achieves an approximately 8.8% improvement as compared to pure-text-based classification algorithm. We further introduce an ensemble classifier using the improved summarization algorithm and show that it achieves about 12.9% improvement over pure-text based methods.
1232795
Efficient text classification by weighted proximal SVM
In this paper, we present an algorithm that can classify large-scale text data with high classification quality and fast training speed. Our method is based on a novel extension of the proximal SVM mode (Fung and Mangasarian, 2001). Previous studies on proximal SVM have focused on classification for low dimensional data and did not consider the unbalanced data cases. Such methods will meet difficulties when classifying unbalanced and high dimensional data sets such as text documents. In this work, we extend the original proximal SVM by learning a weight for each training error. We show that the classification algorithm based on this model is capable of handling high dimensional and unbalanced data. In the experiments, we compare our method with the original proximal SVM (as a special case of our algorithm) and the standard SVM (such as SVM light) on the recently published RCV1-v2 dataset. The results show that our proposed method had comparable classification quality with the standard SVM. At the same time, both the time and memory consumption of our method are less than that of the standard SVM.
18211870
Construction of handwriting databases using transcript-based mapping
A recognition-based system was developed for constructing handwriting databases. The system automatically recognizes the word and the character images in handwritten document images by applying a transcript mapping algorithm. The transcript-mapping process is modeled as an optimization problem involving multiple word-segmentation hypotheses, word recognition and word alignment. The extensive experiments show that a large number of character and word images can be automatically extracted with very high reliability.