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Applications of evolutionary computation (EC) techniques to computer vision are drawing increasing interest from researchers. There are many different ways in which EC techniques can be used as an effective aid to solve problems in the computer vision domain. This paper provides a brief review of the opportunities offered by EC to researchers in computer vision, trying to classify them according to...
An evaluation method of human visual impressions in gray scale textures using morphological morphology is proposed. Variations of textures are generated by modifying repetitively arranged objects and configurations of the arrangements of original textures. The variations are presented to human respondents, and similarity of modified textures based on human impressions is evaluated. The results of...
Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear o friction. Diagnosis, treatment and care of pressure ulcers involve high costs for sanitary systems. Accurate wound evaluation is a critical task for optimizing the effectiveness of treatments and care. Clinicians usually evaluate each pressure ulcer by visual inspection of the damaged...
This paper presents and evaluates a new version of the Semantic Mapping method applied to the construction of a hybrid document organization system based on Self-Organizing Maps. The hybrid system uses reduced document vectors generated by Semantic Mapping to training the SOM map, thus reducing the training time without compromising the quality of the document map. We test the hybrid system with different...
Low-power integrated intelligent sensor systems are of increasing interest for the efficient realization of mobile and distributed realizations. Wireless sensor networks (WSN) are one possible example, where long term sensor vigilance and data acquisition and rather sporadic, brief communication phases occur. Mixed-signal realization, in particular exploiting sub-threshold implementation are interesting,...
Information extraction (IE) aims to extract from textual documents only the fragments which correspond to datafields required by the user. In this paper, we present new experiments evaluating a hybrid machine learning approach for IE that combines text classifiers and hidden Markov models (HMM). In this approach, a text classifier technique generates an initial output, which is refined by an HMM,...
The integration of feature selection techniques within the modeling process of a time series forecaster can improve dealing with some usual important problems in this type of tasks, such as noise reduction, the curse of dimensionality and reducing the complexity of both the problem and the solution. In this paper we show how a convenient combination of feature selection procedures with soft computing...
Rapid developments in computing-related technologies have enabled the collection of large amounts of data at unprecedented rates from diverse systems, both natural and engineered. The availability of such data has motivated the development of intelligent systems to gain new insights into how these systems work, leading thereby to superior decision making. In this paper we present recent advances in...
The use of feature selection can improve accuracy, efficiency, applicability and understandability of a learning process and the resulting learner. For this reason, many methods of automatic feature selection have been developed. By using the modularization of feature selection process, this paper evaluates a wide spectrum of these methods and some additional ones created by combination of different...
The dimension of a knowledge domain can impact the use of genetic algorithms to automatically design fuzzy rule bases, since the search space for the genetic algorithm increases exponentially with the number of features. Filters are a possible approach to reduce the number of features. However, the filter approach does not take into consideration the particular aspects of fuzzy logic when selecting...
Logistic regression (LR) has become a widely used and accepted method to analyse binary or multiclass outcome variables, since it is a flexible tool that can predict the probability for the state of a dichotomous variable. A recently proposed LR method is based on the hybridisation of a linear model and evolutionary product-unit neural network (EPUNN) models for binary classification. This produces...
This paper addresses the problem of probability estimation in multiclass classification tasks combining two well known data mining techniques: support vector machines and neural networks. We present an algorithm which uses both techniques in a two-step procedure. The first step employs support vector machines within a one-vs-all reduction from multiclass to binary approach to obtain the distances...
Selective attention to visual or auditory stimuli that elicits steady-state visual or auditory responses (SSVEP or ASSR respectively) amplifies the power of those flickering frequencies of the stimuli measured in the electroencephalography (EEG). The design of brain-computer interfaces (BCI) based on selective attention to auditory stimuli that elicits ASSRs has two major advantages: First, no much...
This paper presents a memetic algorithm based new approach to feature selection in face recognition. In this work, principal component analysis (PCA) has been used for dimensionality reduction/feature extraction and memetic algorithms have been applied for selection of features in face recognition application. ORL face database has been used for performing the experiments. The results indicate that...
In this paper we present a robotic head MIRA (multimodal interactive robot agent) which has been developed for studying the learning of human robot interaction and improving our understanding of human robot interaction techniques. In this paper we focus on two main aspects of the system; first, we describe how the robot head learns to recognise faces for supporting the interaction process between...
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