Cornelio Yáñez-Márquez

Alpha---Beta bidirectional associative memories: theory and applications

In this work a new Bidirectional Associative Memory model, surpassing every other past and current model, is presented. This new model is based on Alpha–Beta associative memories, from whom it inherits its name. The main and most important characteristic of Alpha–Beta bidirectional associative memories is that they exhibit perfect recall of all patterns in the fundamental set, without requiring the fulfillment of any condition. The capacity they show is 2min(n,m), being n and m the input and output patterns dimensions, respectively. Design and functioning of this model are mathematically founded, thus demonstrating that pattern recall is always perfect, with no regard to the trained pattern characteristics, such as linear independency, orthogonality, or Hamming distance. Two applications illustrating the optimal functioning of the model are shown: a translator and a fingerprint identifier.

Predictive accuracy comparison of fuzzy models for software development effort of small programs

Regression analysis to generate predictive equations for software development effort estimation has recently been complemented by analyses using less common methods such as fuzzy logic models. On the other hand, unless engineers have the capabilities provided by personal training, they cannot properly support their teams or consistently and reliably produce quality products. In this paper, an investigation aimed to compare personal Fuzzy Logic Models (FLM) with a Linear Regression Model (LRM) is presented. The evaluation criteria were based mainly upon the magnitude of error relative to the estimate (MER) as well as to the mean of MER (MMER). One hundred five small programs were developed by thirty programmers. From these programs, three FLM were generated to estimate the effort in the development of twenty programs by seven programmers. Both the verification and validation of the models were made. Results show a slightly better predictive accuracy amongst FLM and LRM for estimating the development effort at personal level when small programs are developed.

An associative memory approach to medical decision support systems

Classification is one of the key issues in medical diagnosis. In this paper, a novel approach to perform pattern classification tasks is presented. This model is called Associative Memory based Classifier (AMBC). Throughout the experimental phase, the proposed algorithm is applied to help diagnose diseases; particularly, it is applied in the diagnosis of seven different problems in the medical field. The performance of the proposed model is validated by comparing classification accuracy of AMBC against the performance achieved by other twenty well known algorithms. Experimental results have shown that AMBC achieved the best performance in three of the seven pattern classification problems in the medical field. Similarly, it should be noted that our proposal achieved the best classification accuracy averaged over all datasets.

POLLUTANTS TIME-SERIES PREDICTION USING THE GAMMA CLASSIFIER

In this work we predict time series of air pollution data taken in Mexico City and the Valley of Mexico, by using the Gamma Classifier which is a novel intelligent associative mathematical model, coupled with an emergent coding technique. Historical and current data about the concentration of specific pollutants, in the form of time series, were used. The pollutants of interest are: carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), and nitrogen oxides (NOx, including both nitrogen monoxide, NO, and nitrogen dioxide, NO2).

A novel associative model for time series data mining

We introduce a novel non-linear forecasting technique based on the Gamma classifier.Its performance for long-term time horizons was tested on synthetic and real data.Two benchmark time series were used for testing.Six time series related to monthly oil production were also used.The Gamma classifier model outperformed previous techniques in forecast accuracy. The paper describes a novel associative model for time series data mining. The model is based on the Gamma classifier, which is inspired on the Alpha-Beta associative memories, which are both supervised pattern recognition models. The objective is to mine known patterns in the time series in order to forecast unknown values, with the distinctive characteristic that said unknown values may be towards the future or the past of known samples. The proposed model performance is tested both on time series forecasting benchmarks and a data set of oil monthly production. Some features of interest in the experimental data sets are spikes, abrupt changes and frequent discontinuities, which considerably decrease the precision of traditional forecasting methods. As experimental results show, this classifier-based predictor exhibits competitive performance. The advantages and limitations of the model, as well as lines of improvement, are discussed.

Bidirectional associative memories: Different approaches

Bidirectional Associative Memories (BAM) are systems that allow to associate pairs of patterns. Once a memory has learned, patterns can be recalled in two directions. BAMs have many applications in pattern recognition and image processing. The aim of this survey is to present several models of BAM throughout time, since Kosko [1988] proposed the first model; followed by those works based on or inspired by it, trying to improve recall capacity; to some recent one-shot models—such as Morphological BAM and Alpha-Beta BAM—which are of particular interest, given their superior performance.

Analysis and Prediction of Air Quality Data with the Gamma Classifier

In later years, different environmental phenomena have attracted the attention of artificial intelligence and machine learning researchers. In particular, several research groups have applied genetic algorithms and artificial neural networks to the analysis of data related to atmospheric and environmental sciences. In the current work, the results of applying the Gamma classifier to the analysis and prediction of air quality data related to the Mexico City Air Quality Metropolitan Index (IMECA in Spanish) are presented.

A new model of BAM: alpha-beta bidirectional associative memories

Most models of Bidirectional associative memories intend to achieve that all trained pattern correspond to stable states; however, this has not been possible. Also, none of the former models has been able to recall all the trained patterns. In this work we introduce a new model of bidirectional associative memory which is not iterative and has no stability problems. It is based on the Alpha-Beta associative memories. This model allows, besides correct recall of noisy patterns, perfect recall of all trained patterns, with no ambiguity and no conditions. An example of fingerprint recognition is presented.

Collaborative learning in postgraduate level courses

Pedagogical experiences in postgraduate level courses are presented.Students use computational tools and repositories in collaborative learning.Problems in medical environments, pollutants, and concept lattices are tackled.The enhancement of teaching-learning processes by collaborative learning is shown. Nowadays, we are immersed in the social and mobile networks era. As a positive consequence of this, collaborative and mobile learning in educational environments have been encouraged thanks to the use of computing for human learning. By coupling the advantages of collaborative and mobile learning, the teaching-learning processes involved in postgraduate courses may be greatly enhanced. The pedagogical experiences in this regard lived by the authors in the Alpha-Beta Research Group when coupling collaborative and mobile learning in the context of postgraduate level courses, are presented in this paper.

One-hot vector hybrid associative classifier for medical data classification.

Pattern recognition and classification are two of the key topics in computer science. In this paper a novel method for the task of pattern classification is presented. The proposed method combines a hybrid associative classifier (Clasificador Híbrido Asociativo con Traslación, CHAT, in Spanish), a coding technique for output patterns called one-hot vector and majority voting during the classification step. The method is termed as CHAT One-Hot Majority (CHAT-OHM). The performance of the method is validated by comparing the accuracy of CHAT-OHM with other well-known classification algorithms. During the experimental phase, the classifier was applied to four datasets related to the medical field. The results also show that the proposed method outperforms the original CHAT classification accuracy.