In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function network. Our approach differs from the pre...
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ISBN:
(纸本)0262100657
In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function network. Our approach differs from the previous complexity regularization neural network function learning schemes in that we operate with random covering numbers and l1 metric entropy, making it possible to consider much broader families of activation functions, namely functions of bounded variation. Some constraints previously imposed on the network parameters are also eliminated this way. The network is trained by means of complexity regularization involving empirical risk minimization. Bounds on the expected risk in terms of the sample size are obtained for a large class of loss functions. Rates of convergence to the optimal loss are also derived.
We present a data integration and datamining platform, called BioGraph, for knowledge discovery in the biomedical domain. BioGraph allows for the automated formulation of comprehensible functional hypotheses relating ...
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In many disciplines, such as social and behavioral sciences, we often have to do ordinal classification by assigning objects to ordinal classes. The fundamental objective of ordinal classification is to create an orde...
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ISBN:
(纸本)0780375084
In many disciplines, such as social and behavioral sciences, we often have to do ordinal classification by assigning objects to ordinal classes. The fundamental objective of ordinal classification is to create an ordering in the universe of discourse. As such, a decision tree for ordinal classification should aim at producing an ordering which is most consistent with the implicit ordering in the input data. Ordinal classification problems are often dealt with by treating ordinal classes as nominal classes, or by representing the classes as values on a quantitative scale. Such approaches may not lead to the most desirable results since the methods do not fit the type of data, viz. ordinal data, concerned. In this paper, we propose a new measure for assessing the quality of output from an ordinal classification approach. We also propose an induction method to generate an ordinal decision tree for ordinal classification based on this quality perspective. We demonstrate the advantage of our method using results from a set of experiments.
It is well known that for the case of a countable partial order, the ideal completion and the chain completion coincide. We investigate the boundary at which the chain and ideal completion do not coincide. We show in ...
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The method of complexity regularization is applied to one hidden-layer radial basis function networks to derive regression estimation bounds and convergence rates for classification. Bounds on the expected risk in ter...
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Most database management systems maintain statistics on the underlying relation. One of the important statistics is that of the "hot items" in the relation: those that appear many times (most frequently, or ...
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Most database management systems maintain statistics on the underlying relation. One of the important statistics is that of the "hot items" in the relation: those that appear many times (most frequently, or more than some threshold). For example, end-biased histograms keep the hot items as part of the histogram and are used in selectivity estimation. Hot items are used as simple outliers in data mining, and in anomaly detection in networking applications. We present a new algorithm for dynamically determining the hot items at any time in the relation that is undergoing deletion operations as well as inserts. Our algorithm maintains a small space data structure that monitors the transactions on the relation, and when required, quickly outputs all hot items, without rescanning the relation in the database. With user-specified probability, it is able to report all hot items. Our algorithm relies on the idea of "group testing", is simple to implement, and has provable quality, space and time guarantees. Previously known algorithms for this problem that make similar quality and performance guarantees can not handle deletions, and those that handle deletions can not make similar guarantees without rescanning the database. Our experiments with real and synthetic data shows that our algorithm is remarkably accurate in dynamically tracking the hot items independent of the rate of insertions and deletions.
Edge computing revolutionizes computer vision, but faces challenges due to computational demands and complexities in deployment and platform integration. In addressing these hurdles, container technology emerges as a ...
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Fuzzy random variable has been defined in several ways in literature. This paper presents a new definition of fuzzy random variable, and gives a novel definition of scalar expected value operator for fuzzy random vari...
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Fuzzy random variable has been defined in several ways in literature. This paper presents a new definition of fuzzy random variable, and gives a novel definition of scalar expected value operator for fuzzy random variables. Some properties concerning the measurability of fuzzy random variable are also discussed. In addition, the concept of independent and identically distributed fuzzy random variables is introduced. Finally, a type of law of large numbers is proved.
Given a set of sensors in a plane or in higher dimension, the strong minimum energy topology problem is to assign transmission range to each of the sensor nodes, so as to minimize the total power consumption. Here the...
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In this paper we have considered the channel assignment problem in multi-channel multi-radio wireless mesh networks. The problem is to assign channels to links in the network with the original topology preservation, a...
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