The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The problem of object recognition may be cast into a spatial grammar framework. The system comprises three novel elements: a spatial organisation of line features, an efficient two dimensional parsing engine, and a genetic algorithm learning routine that induces spatial grammars. Labelling the spatial organisation of feature pairs allows the terminal symbols of the spatial grammar to be defined, and...
One of the possibilities for improving decision processes, and the knowledge management across interacting organizations is to explore successful past experiences. Case-based reasoning (CBR) is a problem solving strategy which is based on the reuse of past solutions (cases) to address new problems. Ontologies are a means to facilitate sharing and reuse of bodies of knowledge across organizations and...
Biomedical research has been revolutionized by high-throughput techniques and the enormous amount of biological data they are able to generate. In particular micro-array technology has the capacity to monitor changes in RNA abundance for thousands of genes simultaneously. The interest shown over microarray analysis methods has rapidly raised. Clustering is widely used in the analysis of microarray...
Microarray technology allows to measure the expression levels of thousands of genes in an experiment. The use of computational methods is fundamental in cancer research. One of the possibilities is the use of artificial intelligence techniques. Several of these techniques have been used to analyze expression arrays. This paper presents a case-based reasoning (CBR) system for automatic classification...
This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical data set contains many categorical data and alternative methods as RS-PCA are required. Thus, we propose to hybridize RS-PCA (Regular...
Machine scheduling is a critical problem in industries where products are custom-designed. The wide range of products, the lack of previous experiences in manufacturing, and the several conflicting criteria used to evaluate the quality of the schedules define a huge search space. Furthermore, production complexity and human influence in each manufacturing step make time estimations difficult to obtain...
One of the main kinds of computational tasks regarding gene expression data is the construction of classifiers (models), often via some machine learning (ML) technique and given data sets, to automatically discriminate expression patterns from cancer (tumor) and normal tissues or from subtypes of cancers. A very distinctive characteristic of these data sets is its high dimensionality and the fewer...
Dynamic programming has provided a powerful approach to optimization problems, but its applicability has been somewhat limited because of the large computational requirements of the standard computational algorithm. In recent years a number of new procedures with reduced computational requirements have been developed. This paper presents a association of a modified Hopfield neural network, which is...
This paper proposes a new sampling procedure for rapidly-exploring random trees (RRT). In traditional path planning methods, sampling procedure is carried out by neglecting the configuration of environment. Hence useless samples tend to be generated; which will result in a waste of computational resources without any considerable improvement in the results. The sampling method proposed in this paper...
This paper examines the formation of self-organizing feature maps (SOFM) by the direct optimization of a cost function through a genetic algorithm (GA). The resulting SOFM is expected to produce simultaneously a topologically correct mapping between input and output spaces and a low quantization error. The proposed approach adopts a cost (fitness) function which is a weighted combination of indices...
Artificial immune systems (AIS) constitute an emerging and promising field, and have been applied to pattern recognition and classification tasks to a limited extent so far. This work is a first attempt of applying the clonal selection principle to the training of multi-layer perceptrons (MLPs). The clonal selection based neural classifier (CSNC) uses the basic concepts of clonal selection to evolve...
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...
Fatigued bills have harmful influence on daily operation of automated teller machine (ATM). To make the fatigued bills classification more efficient, development of an automatic fatigued bill classification method is desired. In this paper, we propose a new method to estimate fatigue levels of bills from feature-selected acoustic energy pattern of banking machines by using the supervised SOM. The...
A new neural network-based approach is proposed to estimate motion hierarchy in image sequences taking into consideration motion discontinuities. The network consists in an input layer, an intermediate layer and an output layer. In order to estimate the most likely displacement at each pixel, we have transposed the block matching approach into the neural network approach and add mechanisms to detect...
We propose a supervised approach to word sense disambiguation based on neural networks combined with evolutionary algorithms. Large tagged datasets for every sense of a polysemous word are considered, and used to evolve an optimized neural network that correctly disambiguates the sense of the given word considering the context in which it occurs. The viability of the approach has been demonstrated...
The need to consider data that contain information that cannot be represented by classical models has led to the development of symbolic data analysis (SDA). As a particular case of symbolic data, symbolic interval time series are interval-valued data which are collected in a chronological sequence through time. This paper presents two approaches to symbolic interval time series analysis. The first...
Creativity has a fundamental role in music composition. One of the theories, which exist about creativity, is combination-theory. In this paper the suitability of genetic algorithms and recurrent neural networks for modeling this theory is considered. We discuss that two phases of combination occurs: one at the genetic algorithm level, and the other at the network level. One important challenge in...
This paper presents an application of neural network interleaved training algorithm proposed in in the domain of chess. In order to use the referenced learning method a structure of metric space is introduced in the space of chess moves. Neural network is used as a classifier of a distance from a given move to the optimal one, leading to significant limitation of the set of moves potentially worth...
This paper presents a hybrid efficient method namely hybrid immune algorithm (HIA) based on artificial immune algorithm (AIA) and bacterial optimization for clustering problems. Four local searches on the basis of heuristic rules for the given clustering problem are designed and applied. This proposed method is implemented and tested on two real datasets. Further, its performance is compared with...
In this paper we propose a multi-objective genetic algorithm to generate Mamdani fuzzy rule-based systems with optimal trade-offs between complexity and accuracy. The main novelty of the algorithm is that both rule base and granularity of the uniform partitions defined on the input and output variables are learned concurrently. To this aim, we exploit a chromosome composed of two parts, which codify...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.