Clustering is an important unsupervised learning paradigm, but so far the traditional methodologies are mostly based on the minimization of the variance between the data and the cluster means. Here we propose a new ev...
详细信息
Determination of the functions of all expressed proteins represents one of the major upcoming challenges in computational molecular biology. Since subcellular location plays a crucial role in protein function, the ava...
详细信息
In almost every area of human activity, the formation of huge databases has created a massive request for new tools to transform data into task oriented knowledge. Our work concentrates on real-world problems, where t...
详细信息
作者：
thompson, SBT Labs
Intelligent Business Syst Res Grp Ipswich IP5 7RE Suffolk England
Ensemble classifiers and algorithms for learning ensembles have recently received a great deal of attention in the machine learning literature (R.E. Schapire, Machine learning 5(2) (1990) 197-227;N. Cesa-Bianchi, Y. F...
详细信息
Ensemble classifiers and algorithms for learning ensembles have recently received a great deal of attention in the machine learning literature (R.E. Schapire, Machine learning 5(2) (1990) 197-227;N. Cesa-Bianchi, Y. Freund, D. Haussler, D.P. Helbold, R.E. Schapire, M.K. Warmuth, Proceedings of the 25th Annual ACM Symposium on the theory of Computing, 1993, pp. 382-391;L. Breiman, Bias, Technical Report 460, Statistics Department, University of California, Berkeley, CA, 1996;J.R. Quinlan, Proceedings of the 14thinternationalconference on Machine learning, Italy, 1997;Y. Freund, R.E. Schapire, Proceedings of the 13thinternationalconference on Machine learning ICML96, Bari, Italy 1996, pp. 148-157;A.J.C. Sharkey, N.E. Sharkey, Combining diverse neural nets, the Knowledge engineering Review 12 (3) (1997) 231-247). In particular, boosting has received a great deal of attention as a mechanism by which an ensemble of classifiers that has a better generalisation characteristic than any single classifier derived using a particular technique can be discovered. In this article, we examine and compare a number of techniques for pruning a classifier ensemble which is overfit on its training set and find that a real valued GA is at least as good as the best heuristic search algorithm for choosing an ensemble weighting. (C) 1999 Elsevier Science B.V. All rights reserved.
We present a statistical learning algorithm for synthesizing random sound textures resembling an input sound texture segment. Our approach begins by constructing a hierarchical multi-resolution representation of the i...
详细信息
A new system to create music from audio-visual environmental data is proposed. the system consists of an image analyzer, sound analyzer, and music generator. these audio-visual components work together to generate mus...
详细信息
A flexible capacitive normal/shear force sensor design is presented. Fabrication on a flexible polyimide substrate allows the sensor to be molded to non-planar gripper surfaces. the ability to measure normal and shear...
详细信息
Cluster Analysis is a technique which is used to discover patterns and associations within data. More specifically, it is a multivariate statistical procedure that starts with a data set containing information on some...
详细信息
ISBN:
(纸本)081862440X
Cluster Analysis is a technique which is used to discover patterns and associations within data. More specifically, it is a multivariate statistical procedure that starts with a data set containing information on some variables and attempts to reorganize these data cases into relatively homogeneous groups. One of the major problems encountered by researchers, with regard to cluster analysis, is that different clustering methods can and do generate different solutions for the same data set. What is obviously needed, is a technique that has discovered the most 'natural' groups in a data set. Genetic Algorithms belong to a class of 'artificially intelligent' techniques, that are founded on principles of natural selection and natural genetics. the primary goal of this research effort is to investigate the potential feasibility of using genetic algorithms for the purpose of clustering.
the authors present a non-lethal high-voltage intelligent intrusion detection sensor for perimeter alarm systems. the sensor looks like a conventional high-voltage electric net barrier. It is a kind of outdoor detecti...
详细信息
the authors present a non-lethal high-voltage intelligent intrusion detection sensor for perimeter alarm systems. the sensor looks like a conventional high-voltage electric net barrier. It is a kind of outdoor detection system, and functions not only as a fearful obstacle but also as an intrusion detection sensor. the alarm detection algorithm, which classifies the sensor readings into three levels, alarm, uncertain, and secure, avoids many false alarms by detecting logically unreasonable data and analyzing weather-related disturbance. By this algorithm, the system makes a significant improvement in reducing the false alarm rate. this approach vitalizes the conventional simple high-voltage barrier and upgrades it into an intelligent intrusion detection system.
A digital methodology called single error correction-single error detection (SECSED) is used to improve the learning and recall rules of the conventional Hopfield neural network. the XOR operator is used to implement ...
详细信息
A digital methodology called single error correction-single error detection (SECSED) is used to improve the learning and recall rules of the conventional Hopfield neural network. the XOR operator is used to implement the learning phase and the recall phase of the modified network. the bulk data processing of the modified Hopfield network is much faster than that of the conventional Hopfield network, because the improved method uses only XOR operations to avoid the carry-propagation of the multiplication and addition which is necessary in the learning and recall phases in the conventional Hopfield network. Because only the XOR operation is involved, it is very suitable for VLSI implementation of the neural network. Simulated results indicate that the modified Hopfield neural network can recognize each 24% noise-interfered pattern within 1 to 6 iterations with a recognition rate above 90%, while the conventional Hopfield neural network recognizes each small noise-interfered pattern with 6 to 12 iterations and a recognition rate below 5%.< >
暂无评论