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- B. John Oommen, Luis Rueda
- Pattern Recognition
- 2006

In this paper, we formally present a novel estimation method, referred to as the Stochastic Learning Weak Estimator (SLWE), which yields the estimate of the parameters of a binomial distribution, where the convergence of the estimate is weak, i.e. with regard to the rst and second moments. The estimation is based on the principles of stochastic learning.… (More)

- Luis Rueda, Iman Rezaeian
- BMC Bioinformatics
- 2010

Processing cDNA microarray images is a crucial step in gene expression analysis, since any errors in early stages affect subsequent steps, leading to possibly erroneous biological conclusions. When processing the underlying images, accurately separating the sub-grids and spots is extremely important for subsequent steps that include segmentation,… (More)

- Luis Rueda, Myriam Herrera
- Pattern Recognition
- 2008

Linear dimensionality reduction (LDR) techniques are quite important in pattern recognition due to their linear time complexity and simplicity. In this paper, we present a novel LDR technique which, though linear, aims to maximize the Chernoff distance in the transformed space; thus, augmenting the class separability in such a space. We present the… (More)

- Luis Rueda
- SSPR/SPR
- 2008

Optimal multilevel thresholding is a quite important problem in image segmentation and pattern recognition. Although efficient algorithms have been proposed recently, they do not address the issue of irregularly sampled histograms. A polynomial-time algorithm for multilevel thresholding of irregularly sampled histograms is proposed. The algorithm is… (More)

- Luis Rueda, Myriam Herrera
- CIARP
- 2006

Linear dimensionality reduction (LDR) is quite important in pattern recognition due to its efficiency and low computational complexity. In this paper, we extend the two-class Chernoff-based LDR method to deal with multiple classes. We introduce the criterion, as well as the algorithm that maximizes such a criterion. The proof of convergence of the algorithm… (More)

- Luis Rueda, B. John Oommen, Claudio Henríquez
- Pattern Recognition
- 2010

Linear Dimensionality Reduction (LDR) techniques have been increasingly important in Pattern Recognition (PR) due to the fact that they permit a relatively simple mapping of the problem onto a lower-dimensional subspace, leading to simple and computationally efficient classification strategies. Although the field has been well developed for the two-class… (More)

- B. John Oommen, Luis Rueda
- Artif. Intell.
- 2005

Many optimization problems in computer science have been proven to be NP-hard, and it is unlikely that polynomial-time algorithms that solve these problems exist unless P = NP. Alternatively, they are solved using heuristics algorithms, which provide a sub-optimal solution that, hopefully, is arbitrarily close to the optimal one. Such problems are found in… (More)

- Luis Rueda, B. John Oommen
- Inf. Sci.
- 2006

Adaptive coding techniques have been increasingly used in lossless data compression. They are suitable for a wide range of applications, in which on-line compression is required, including communications, internet, e-mail, and e-commerce. In this paper, we present an adaptive Fano coding method applicable to binary and multi-symbol code alphabets. We… (More)

- Luis Rueda, B John Oommen
- IEEE transactions on systems, man, and…
- 2006

This correspondence shows that learning automata techniques, which have been useful in developing weak estimators, can be applied to data compression applications in which the data distributions are nonstationary. The adaptive coding scheme utilizes stochastic learning-based weak estimation techniques to adaptively update the probabilities of the source… (More)

- Luis Rueda, Yuanquan Zhang
- Pattern Recognition
- 2006

Fuzzy-clustering methods, such as fuzzy k-means and Expectation Maximization, allow an object to be assigned to multiple clusters with different degrees of membership. However, the memberships that result from fuzzy-clustering algorithms are difficult to be analyzed and visualized. The memberships, usually converted to 0-1 values, are visualized using… (More)