Growing Cell Structures Network Research Articles

A Biologically Inspired System for Classification of Natural Images

A system for visual information analysis and classification based on a biologically inspired visual selective attention model with knowledge structuring is presented. The system is derived from well-known analogous processes in the visual system of primates and inference procedures of the human brain. It consists of three main units: biologically inspired visual selective attention, knowledge structuring, and clustering of visual information. The biologically inspired visual selective attention unit closely follow the mechanisms of the visual what pathway and where pathway in the primates' brain. It uses a bottom-up approach to generate a salient area based on low-level features extracted from natural images. The scale selection to determine suitable size of salient areas uses a maximum entropy approach. This unit also contains a low-level top-down selective attention module that performs decisions on interesting objects by human interaction. In this module, a reinforcement/inhibition mechanism is exploited. The knowledge structuring unit automatically creates a relevance map from salient image areas generated by the biologically inspired unit. It also derives a set of well-structured representations from low-level descriptions to drive the final classification. The knowledge structuring unit relys on human knowledge to produce suitable links between low-level descriptions and high-level representation on a limited training set. The backbone of this unit is a distribution mapping strategy involving two basic modules: structured low-level feature extraction using convolution neural network and a topology representation module based on a growing cell structure network. The third unit of the system classification is achieved by simulating high-level top-down visual information perception and clustering using an incremental Bayesian parameter estimation method. The proposed modular system architecture offers straightforward expansion to include user relevance feedback, contextual input, and multimodal information if available

Gene‐CBR: A CASE‐BASED REASONIG TOOL FOR CANCER DIAGNOSIS USING MICROARRAY DATA SETS

Gene expression profiles are composed of thousands of genes at the same time, representing the complex relationships between them. One of the well‐known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments or cases. In this context, the ability of design methods capable of overcoming current limitations of state‐of‐the‐art algorithms is crucial to the development of successful applications. This paper presents gene‐CBR, a hybrid model that can perform cancer classification based on microarray data. The system employs a case‐based reasoning model that incorporates a set of fuzzy prototypes, a growing cell structure network and a set of rules to provide an accurate diagnosis. The hybrid model has been implemented and tested with microarray data belonging to bone marrow cases from forty‐three adult patients with cancer plus a group of six cases corresponding to healthy persons.

FSfRT: Forecasting System for Red Tides

A hybrid neuro-symbolic problem-solving model is presented in which the aim is to forecast parameters of a complex and dynamic environment in an unsupervised way. In situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. In such a situation, it has been found that a hybrid case-based reasoning system can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The system employs a case-based reasoning model to wrap a growing cell structures network, a radial basis function network and a set of Sugeno fuzzy models to provide an accurate prediction. Each of these techniques is used at a different stage of the reasoning cycle of the case-based reasoning system to retrieve historical data, to adapt it to the present problem and to review the proposed solution. This system has been used to predict the red tides that appear in the coastal waters of the north west of the Iberian Peninsula. The results obtained from experiments, in which the system operated in a real environment, are presented.

FSfRT: FORECASTING SYSTEM FOR RED TIDES. A HYBRID AUTONOMOUS AI MODEL

A hybrid neuro-symbolic problem-solving model is presented in which the aim is to forecast parameters of a complex and dynamic environment in an unsupervised way. In situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behavior of the system can be a problematic task. In such a situation, it has been found that a hybrid case-based reasoning system can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The system employs a case-based reasoning model that incorporates a growing cell structures network, a radial basis function network, and a set of Sugeno fuzzy models to provide an accurate prediction. Each of these techniques is used at a different stage of the reasoning cycle of the case-based reasoning system to retrieve historical data, to adapt it to the present problem, and to review the proposed solution. This system has been used to predict the red tides that appear in the coastal waters of the north west of the Iberian Peninsula. The results obtained from experiments, in which the system operated in a real environment, are presented.

CBR based system for forecasting red tides

A hybrid neuro-symbolic problem solving model is presented in which the aim is to forecast parameters of a complex and dynamic environment in an unsupervised way. In situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. The system employs a case-based reasoning model to wrap a growing cell structures network, a radial basis function network and a set of Sugeno fuzzy models to provide an accurate prediction. Each of these techniques is used in a different stage of the reasoning cycle of the case-based reasoning system to retrieve, adapt and review the proposed solution to the present problem. This system has been used to predict the red tides that appear in the coastal waters of the north west of the Iberian Peninsula. The results obtained from experiments are presented.

Neural navigation interfaces for Information Retrieval: Are they more than an appealing idea?

Neural networks have recently been proposed for the construction of navigation interfaces for Information Retrieval systems. In this paper, we give an overview of some current research in this area. Most of the cited approaches use (variants) of the well-known Kohonen network. The Kohonen network implements a topology-preserving dimensionality-reducing mapping, which can be applied for information visualization. We identify a number of problems in the application of Kohonen networks for Information Retrieval, most notably scalability, reliability and retrieval effectiveness. To solve these problems we propose to use the Growing Cell Structures network, a variant of the Kohonen network which shows a more flexible adaptation to the domain structure. This network was tested on two standard test-collections, using a combined recall and precision measure, and compared to traditional IR methods such as the Vector Space Model and various clustering algorithms. The network performs at a competitive level of effectiveness, and is suitable for visualization purposes. However, the incremental training procedures for the networks result in a reliability problem, and the approach is computationally intensive. Also, the utility of the resulting maps for navigation will need further improvement.