Catálogo de publicaciones - libros

Classifiers, as one of the important tools of analyzing gene expression data in the post-genomic epoch, have been used widely in the classification of different cancer types in the past few years. Although most existing classifiers have high classification accuracy, the process of classification is a black box and they can not give biologists more information and interpretable results of classification. In this paper, we propose a novel visualization cancer classification method. Besides offering high classification accuracy, the method can help us identify complex disease-related genes and assess gene expression variation during the process of classification. The results of classification are natural and interpretable and the process of classification is visible. To evaluate the performance of the method we have applied the proposed method to three public data sets. The experimental results demonstrate that the approach is feasible and useful.

With the dramatic development of web technologies, tremendous amount of information become available to users. The great advantages of the web are the ease with which information can be published and made available to a wide audience, and the ability to organize and connect different resources in a graph-based structure using hyperlinks. However, most of these links are created manually and the page that the link represents must be known to the author of the link. In this paper, we propose a decision-tree-based approach to solve this problem. We set up a system that gathers information about the candidate pages, evaluates them and creates links to them automatically.

In this paper, keyword map-based relevance feedback is applied to interactive Blog search.There exists vast amount of information in the Web, from which users usually gather information without definite information needs. In particular, when exploring the Blog space, the range of user’s interests is expected to be broader than usual Web browsing process. The relevance feedback techniques have been studied in the field of document retrieval, aiming to generate appropriate queries for users’ information needs.Although this approach is effective when the assumption that a user has a concrete criteria on the relevance of retrieved documents holds, it could not always hold when searching Blog, which consists of vast number of short articles about various topics. Compared with the previous work on keyword map-based relevance feedback, the algorithm proposed in this paper can consider multiple topics, in which a user is interested on the keyword map.

This paper describes the development of an Automated Knowledge Extraction Agent (AKEA) which was designed to acquire online news and document from the internet for the establishment of a knowledge based crisis communication portal. It was recognized that in times of crisis, an effective communication mechanism is essential to maintain peace and calmness in the community by providing timely and appropriate information. It is proposed that the incorporation of software agents into the crisis communication portal will be capable to send alert news to subscribed users via internet and mobile services. The proposed system consists of crawler, wrapper, name-entity tagger, AIML (Artificial Intelligence Markup language) and an animated character is used in the front-end for human computer communication.

We present a facial recognition system based on a probabilistic approach to adaptive processing of Human Face Tree Structures. Human Face Tree Structures are made up of holistic and localized Gabor Features. We propose extending the recursive neural network model by Frasconi et. al. [1] in which its learning algorithm was carried out by the conventional supervised back propagation learning through the tree structures, by making use of probabilistic estimates to acquire discrimination and obtain smooth discriminant boundaries at the structural pattern recognition. Our proposed learning framework of this probabilistic structured model is hybrid learning in locally unsupervised for parameters in mixture models and in globally supervised for weights in feed-forward models. The capabilities of the model in a facial recognition system are evaluated. The experimental results demonstrate that the proposed model significantly improved the recognition rate in terms of generalization.

It is very difficult to evaluate the performance of computer vision algorithms at present. We argue that visual cognition theory can be used to challenge this task. Following are the reasons: (1) Human vision system is so far the best and the most general vision system; (2) The human eye and camera surely have the same mechanism from the perspective of optical imaging; (3) Computer vision problem is similar to human vision problem in theory; (4) The main task of visual cognition theory is to investigate the principles of human vision system. In this paper, we first illustrate why vision cognition theory can be used to characterize the performance of computer vision algorithms and discuss how to use it. Then from the perspective of computer science we summarize some of important assumptions of visual cognition theory. Finally, many cases are introduced, which show that our method can work reasonably well.

Human vision system can understand images quickly and accurately, but it is impossible to design a generic computer vision system to challenge this task at present. The most important reason is that computer vision community is lack of effective collaborations with visual psychologists, because current object recognition systems use only a small subset of visual cognition theory. We argue that it is possible to put forward a generic solution for image object recognition if the whole vision cognition theory of different schools and different levels can be systematically integrated into an inherent computing framework from the perspective of computer science. In this paper, we construct a generic object recognition solution, which absorbs the pith of main schools of vision cognition theory. Some examples illustrate the feasibility and validity of this solution.

Much evidence from visual psychology suggests that images can be looked as a kind of language, by which image semantics can be unambiguously expressed. In this paper, we discuss the primitives and grammar of image language based on cognition theory. Hence image understanding can surely be manipulated in the same way as language analysis.

We present a discrimation method for seismic events. One event is described by high level features. Since these variables are both quantitative and qualitative, we develop a processing line, on the cross-road of statistics (”Mixtures of Experts”) and Artificial Intelligence (”Fuzzy Inference System”). It can be viewed as an original extension of Radial Basis Function Networks. The method provides an efficient trade-off between high performance and intelligibility. We propose also a graphical presentation of the model satisfying the experts’ requirements for intelligibility.

Present research work relates to a methodology using modified smooth logistic membership function (MF) in finding out fuzzy patterns in multi-level of satisfaction (LOS) for Multiple Criteria Decision-Making (MCDM) problem. Flexibility of this MF in applying to real world problem has been validated through a detailed analysis. An example elucidating an MCDM model applied in an industrial engineering problem is considered to demonstrate the veracity of the proposed methodology. The key objective of this paper is to guide decision makers (DM) in finding out the best candidate-alternative with higher degree of satisfaction with lesser degree of vagueness under tripartite fuzzy environment. The approach presented here provides feedback to the decision maker, implementer and analyst.