This paper is devoted to the use of genetic programming for the search of hypothesis space in visual learning tasks. The long-term goal of our research is to synthesize human-competitive procedures for pattern discrim...
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ISBN:
(纸本)3540423591
This paper is devoted to the use of genetic programming for the search of hypothesis space in visual learning tasks. The long-term goal of our research is to synthesize human-competitive procedures for pattern discrimination by means of learning process based directly on the training set of images. In particular, we introduce a novel concept of evolutionary learning employing, instead of scalar evaluation function, pairwise comparison of hypotheses, which allows the solutions to remain incomparable in some cases. That extension increases the diversification of the population and improves the exploration of the hypothesis space search in comparison with 'plain' evolutionary computation using scalar evaluation. This supposition is verified experimentally in this study in an extensive comparative experiment of visual learning concerning the recognition of handwritten characters.
Although neural networks have many appealing properties, yet there is neither a systematic way how to set up the topology of a neural network nor how to determine its various learning parameters. Thus an expert is nee...
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We have developed a method for analysis and prognosis of multiparametric kidney function courses. The method combines two abstraction steps (state abstraction and temporal abstraction) with Case-based Reasoning. Recen...
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ISBN:
(纸本)3540423591
We have developed a method for analysis and prognosis of multiparametric kidney function courses. The method combines two abstraction steps (state abstraction and temporal abstraction) with Case-based Reasoning. Recently we have started to apply the same method in the domain of Geomedicine, namely for the prognosis of the temporal spread of diseases, mainly of influenza, where just one of the two abstraction steps is necessary, that is the temporal one. In this paper, we present the application of our method in the kidney function domain, show how we are going to apply the same ideas for the prognosis of the spread of diseases, and summarise the main principles of the method.
Image database systems and image management in general are extremely important in achieving both technical and functional integration of the various clinical functional units. In the emerging 'film-less' clini...
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ISBN:
(纸本)3540423591
Image database systems and image management in general are extremely important in achieving both technical and functional integration of the various clinical functional units. In the emerging 'film-less' clinical environment it is possible to extend the capabilities of diagnostic medical image techniques and introduce intelligent content-based image retrieval operations, towards 'evidence-based' clinical decision support. In this paper we presented an integrated methodology for content-based retrieval of multi-segmented medical images. The system relies on the tight integration of clustering andpattern- (similarity) matching techniques and operations. Evaluation of the approach on a set of indicative medical images shows the reliability of our approach.
The featureless patternrecognition methodology based on measuring some numerical characteristics of similarity between pairs of entities is applied to the problem of protein fold classification. In computational biol...
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ISBN:
(纸本)3540423591
The featureless patternrecognition methodology based on measuring some numerical characteristics of similarity between pairs of entities is applied to the problem of protein fold classification. In computational biology, a commonly adopted way of measuring the likelihood that two proteins have the same evolutionary origin is calculating the so-called alignment score between two amino acid sequences that shows properties of inner product rather than those of a similarity measure. Therefore, in solving the problem of determining the membership of a protein given by its amino acid sequence (primary structure) in one of preset fold classes (spatial structure). we treat the set of all feasible amino acid sequences as a subset of isolated points in an imaginary space in which the linear operations and inner product are defined in an arbitrary unknown manner. but without any conjecture on the dimension, i.e. as a Hilbert space.
A tool and a methodology for datamining in picture archiving systems are presented. It is intended to discover the relevant knowledge for picture analysis and diagnosis from the database of image descriptions. Knowle...
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ISBN:
(纸本)3540423591
A tool and a methodology for datamining in picture archiving systems are presented. It is intended to discover the relevant knowledge for picture analysis and diagnosis from the database of image descriptions. Knowledge engineering methods are used to obtain a list of attributes for symbolic image descriptions. An expert describes images according to this list, and stores descriptions in the database. Digital image processing can be applied to improve imaging of specific image features or to get expert-independent feature evaluation. Decision tree induction is used to team the expert knowledge, presented in the form of image descriptions in the database. Constructed decision tree presents effective models of decision-making, which can be learned to support image classification by the expert. A tool for datamining and image processing is presented. The developed tool and methodology have been tested in the task of early differential diagnosis of pulmonary nodules in lung tomograms and was effective for preclinical diagnosis of peripheral lung cancer, so that we applied the developed methodology of datamining in other medical tasks such as lymph node diagnosis in MRI and investigation of breast MRI.
An information-statistical approach is proposed for analyzing temporal-spatial data. The basic idea is to analyze the temporal aspect of the data by first conditioning on specific spatial nature of the data. Parametri...
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In recent years feedback approaches have been used in relating low-level image features with concepts to overcome the subjective nature of the human image interpretation. Generally, in these systems when the user star...
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ISBN:
(纸本)3540423591
In recent years feedback approaches have been used in relating low-level image features with concepts to overcome the subjective nature of the human image interpretation. Generally, in these systems when the user starts with a new query, the entire prior experience of the system is lost. In this paper, we address the problem of incorporating prior experience of the retrieval system to improve the performance on future queries. We propose a semi-supervised fuzzy clustering method to learn class distribution (meta knowledge) in the sense of high-level concepts from retrieval experience. Using fuzzy rules, we incorporate the meta knowledge into a probabilistic relevance feedback approach to improve the retrieval performance. Results presented on synthetic and real databases show that our approach provides better retrieval precision compared to the case when no retrieval experience is used.
machinelearning algorithms used in early fault detection for centrifugal pumps make it possible to better exploit the information content of measured signals, making machine monitoring more economical and application...
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ISBN:
(纸本)3540423591
machinelearning algorithms used in early fault detection for centrifugal pumps make it possible to better exploit the information content of measured signals, making machine monitoring more economical and application-oriented. The total amount of sensors is reduced by exhausting the information derived from the sensors far beyond the scope of traditional engineering through the application of various features and high-dimensional decision-making. The feature selection plays a crucial role in modelling an early fault detection system. Due to presence of noisy features with outliers and correlations between features a correctly determined subset of features will distinctly improve the classification rate. In addition the requirements for the hardware to monitor the pump decrease therefore its price. Wrappers and filters, the two major approaches for feature selection described in literature [4] will be investigated and compared using real-world data.
The diagnostic investigation of immunologically influenced diseases includes the determination of serological and cellular parameters in the peripheral blood of patients. For the detection of these parameters, a varie...
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ISBN:
(纸本)3540423591
The diagnostic investigation of immunologically influenced diseases includes the determination of serological and cellular parameters in the peripheral blood of patients. For the detection of these parameters, a variety of well established and new fashioned immunoassays are available. Since these test kits have been shown to yield highly different results of unknown clinical significance, we have compared a selection of commercial test kits and have analysed their diagnostic value by datamining. Here we describe applications of datamining for the diagnosis of inflammatory and thrombotic induced acute central nervous processes and identification of various prognostic groups of cancer patients. Evaluation of laboratory results by datamining revealed a restricted suitability of chosen test parameters to reply diagnostic questions. Thereby, unnecessarily performed test systems could be removed from the diagnostic panel, Furthermore, computer assisted classification in positive and negative results according to clinical findings could be implemented.
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