Real life transaction data often miss some occurrences of items that are actually present. As a consequence some potentially interesting frequent patterns cannot be discovered, since with exact matching the number of ...
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Recently applying artificial intelligence, machinelearning and data mining techniques to intrusion detection system are increasing. But most of researches are focused on improving the performance of classifier. Selec...
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the support vector machine (SVM) is considered here in the context of pattern classification, the emphasis is on the soft margin classifier which uses regularization to handle non-separable learning samples. We presen...
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ISBN:
(纸本)0769521428
the support vector machine (SVM) is considered here in the context of pattern classification, the emphasis is on the soft margin classifier which uses regularization to handle non-separable learning samples. We present an SVM parameter estimation algorithm that first identifies a subset of,the learning samples that we call the support set and then determines not only the weights of the classifier but, also the hyperparameter that controls the influence of the regularizing penalty term, on basis thereof. We provide numerical results using several data sets from the public domain.
the C4.5 Decision Tree and Naive Bayes learners are known to produce unreliable probability forecasts. We have used simple Binning [11] and Laplace Transform [2] techniques to improve the reliability of these learners...
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ISBN:
(纸本)0769521428
the C4.5 Decision Tree and Naive Bayes learners are known to produce unreliable probability forecasts. We have used simple Binning [11] and Laplace Transform [2] techniques to improve the reliability of these learners and compare their effectiveness withthat of the newly developed Venn Probability machine (VPM) meta-learner [9]. We assess improvements in reliability using loss functions, Receiver Operator Characteristic (ROC) curves and Empirical Reliability Curves (ERC). the VPM outperforms the simple techniques to improve reliability, although at the cost of increased computational intensity and slight increase in error rate. these trade-offs are discussed.
Breast self-examination (BSE) is a non-invasive, self-administered and simple screening procedure for detecting breast cancer at an early stage. this procedure can be performed in private and at any time. A variety of...
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Breast self-examination (BSE) is a non-invasive, self-administered and simple screening procedure for detecting breast cancer at an early stage. this procedure can be performed in private and at any time. A variety of leaflets and websites exist, which attempt to train women on how to perform BSE. there are also some learning systems consisting of videos and audio cassettes. However, there are no fully interactive systems in existence and no real-time feedback is given to a user on whether she is correctly performing the procedure. We aim to develop an intelligent interactive multimedia system incorporating patternrecognition and machine vision techniques, and provide real-time feedback to assist and guide women to perform BSE accurately. Using her hand in a specific configuration to conduct palpation of the breasts is the basic means available for a woman to perform BSE. However, a human hand is highly articulated and deformable with 27 degree-of-freedom parameters according to its anatomy. Recognising hand gestures and postures is a challenging task that has been studied in many areas and applications. In this paper, the simplified three-dimensional (3D) hand model is presented, which has only 8 degree-of-freedom parameters and is especially adapted for use withthe breast self-examination system. this model will be a potentially effective simulation and tracking tool that will contribute to BSE learning and thus to the development of an intelligent fully interactive BSE system.
Assisted document recognition systems have to integrate automatic recognition, manual edition and incremental learning in a single interactive environment. this paper raises the question of the organization of these t...
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Assisted document recognition systems have to integrate automatic recognition, manual edition and incremental learning in a single interactive environment. this paper raises the question of the organization of these three kinds of operations. When an analyzer has the ability to improve with use, there is a tradeoff between the benefits of enhancing the accuracy of automatic analysis, and the additional time spent in interacting for feedback communication. the global cost depends then on the sequence of processed entities, and on the relevance of the learning transactions. Notations are introduced to describe the evolution of a recognition session, and possible organization strategies are discussed. then a cost model is presented to allow the comparison between different organization schemes. We describe some concrete experiments of cost measures withthe ApOFIS font identification tool and the ScanWorX OCR;the first results show that a user-driven approach can potentially save substantial effort in the recognition process, in comparison withmachine-driven systems.
the two-volume set CCIS 1869 and 1870 constitutes the refereed proceedings of the 4thinternationalconference on Neural Computing for Advanced Applications, NCAA 2023, held in Hefei, China, in July 2023.;the 83 full ...
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ISBN:
(数字)9789819958443
ISBN:
(纸本)9789819958436
the two-volume set CCIS 1869 and 1870 constitutes the refereed proceedings of the 4thinternationalconference on Neural Computing for Advanced Applications, NCAA 2023, held in Hefei, China, in July 2023.;the 83 full papers and 1 short paper presented in these proceedings were carefully reviewed and selected from 211 submissions. the papers have been organized in the following topical sections: Neural network (NN) theory, NN-based control systems, neuro-system integration and engineering applications; machinelearning and deep learning for data mining and data-driven applications; Computational intelligence, nature-inspired optimizers, and their engineering applications; Deep learning-driven patternrecognition, computer vision and its industrial applications; Natural language processing, knowledge graphs, recommender systems, and their applications; Neural computing-based fault diagnosis and forecasting, prognostic management, and cyber-physical system security; Sequence learning for spreading dynamics, forecasting, and intelligent techniques against epidemic spreading (2); Applications of Data Mining, machinelearning and Neural Computing in Language Studies; Computational intelligent Fault Diagnosis and Fault-Tolerant Control, and their Engineering Applications; and Other Neural computing-related topics.
the two-volume set CCIS 1869 and 1870 constitutes the refereed proceedings of the 4thinternationalconference on Neural Computing for Advanced Applications, NCAA 2023, held in Hefei, China, in July 2023.;the 83 full ...
详细信息
ISBN:
(数字)9789819958474
ISBN:
(纸本)9789819958467
the two-volume set CCIS 1869 and 1870 constitutes the refereed proceedings of the 4thinternationalconference on Neural Computing for Advanced Applications, NCAA 2023, held in Hefei, China, in July 2023.;the 83 full papers and 1 short paper presented in these proceedings were carefully reviewed and selected from 211 submissions. the papers have been organized in the following topical sections: Neural network (NN) theory, NN-based control systems, neuro-system integration and engineering applications; machinelearning and deep learning for data mining and data-driven applications; Computational intelligence, nature-inspired optimizers, and their engineering applications; Deep learning-driven patternrecognition, computer vision and its industrial applications; Natural language processing, knowledge graphs, recommender systems, and their applications; Neural computing-based fault diagnosis and forecasting, prognostic management, and cyber-physical system security; Sequence learning for spreading dynamics, forecasting, and intelligent techniques against epidemic spreading (2); Applications of Data Mining, machinelearning and Neural Computing in Language Studies; Computational intelligent Fault Diagnosis and Fault-Tolerant Control, and their Engineering Applications; and Other Neural computing-related topics.
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