Antonio B. Bailón

Summarizing Structured Documents through a Fractal Technique

Every day we search new information in the web, and we found a lot of documents which contain pages with a great amount of information. There is a big demand for automatic summarization in a rapid and precise way. Many methods have been used in automatic extraction but most of them do not take into account the hierarchical structure of the documents. A novel method using the structure of the document was introduced by Yang and Wang in 2004. It is based in a fractal view method for controlling the information displayed. We explain its drawbacks and we solve them using the new concept of fractal dimension of a text document to achieve a better diversification of the extracted sentences improving the performance of the method.

Mining generalized temporal patterns based on fuzzy counting

Event-based sequences are a kind of pattern based on temporal associations with two essential characteristics: they are syntactically simple and have a great expressive power. For this reason, event-based sequence mining is an interesting solution to the problem of knowledge discovery in dynamic domains, mainly characterized by a time-varying nature. The inter-transactional model has led to the design of algorithms aimed to obtain this sort of patterns from time-stamped datasets. These algorithms extend the well-known Apriori algorithm, by explicitly adding the temporal context where associations among frequent events occurs. This leads to the possibility of extracting a larger number of patterns with a potential interest in decision making. However, its usefulness is diminished in those datasets where the characteristics of variability and uncertainty are present, which is a common issue in real domains. This is due to the rigidity of the counting method, which uses an exact measure of distance between temporal events. As a solution, we propose a generalization of the temporal mining process, which implies a relaxation of the counting method including the concept of approximate temporal distance between events. In particular, in this paper we present an algorithm, called TSET^f^u^z^z^y-Miner, which incorporates a fuzzy-based counting technique in order to extract general, flexible, and practical temporal patterns taking into account the particular characteristics of real domains.

Continuous classifying associative memory

In this article we present the so‐called continuous classifying associative memory, able to store continuous patterns avoiding the problems of spurious states and data dependency. This is a memory model based on our previously developed classifying associative memory, which enables continuous patterns to be stored and recovered. We will also show that the behavior of this continuous classifying associative memory may be adjusted to some predetermined goals by selecting some internal operating functions.

CLAM: A New Model of Associative Memory

We present a new associative memory model that stores arbitrary bipolar patterns without the problems we can find in other models like BAM or LAM. After identifying those problems we show the new memory topology and we explain its learning and recall stages. Mathematical demonstrations are provided to prove that the new memory model guarantees the perfect retrieval of every stored pattern and also to prove that whatever the input of the memory is, it operates as a nearest neighbor classifier. ©2000 John Wiley & Sons, Inc.

Intelligent Tutoring System, Based on Video E-learning, for Teaching Artificial Intelligence

In the last few years, distant learning is gaining traction as a valid teaching approach taking advantage of the Internet and current multimedia capabilities. Even though thousands of students are enrolling to Massive Open Online Courses, there is still a lack of proper educative programs who account for the individual characteristics of the students. In particular, most e-learning courses tend to be mere repositories of contents, very teacher-centric and lacking the necessary individual personalisation to account for each student’s needs, expectations and paces. In this work, we propose and describe an Intelligent Tutoring System that enables the automatic adaptation of the contents of the course to the particular learners. The systems was tested with a group of students with very positive direct and indirect results.

Storage of linguistic information in a continuous classifying associative memory

In this article, we analyze the use of the continuous classifying associative memory (CCLAM) to store linguistic information. Freedom in the choice of the functions that control the operation of the CCLAM equip this memory with the capacity to adapt to different information storage and recovery needs. We begin with the problem of storing linguistic terms by memorizing the patterns formed by the degrees of compatibility with these terms. After that, the problem of storing linguistic rules is discussed. Let us remark that in these cases not a single CCLAM is used, but rather a set of them connected in suitable structured ways.

A coding method to handle linguistic variables

We present a coding method for linguistic variables which we have named Incremental Discretization. It allows us to express any fuzzy subset of the universe of discourse of the linguistic variable in binary or bipolar terms. This will permit us to process fuzzy information expressed in linguistic terms using discrete models of Artificial Intelligence. In order to test the effectiveness, we apply this method to the memorization of a set of fuzzy rules using models of discrete associative memories.