Titlebook: Nonlinear Estimation and Classification; David D. Denison,Mark H. Hansen,Bin Yu Book 2003 Springer Science+Business Media New York 2003 AN

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Coarse-to-Fine Classification and Scene Labelingny satisfying solution, at least in searching for multiple objects in real, cluttered scenes with arbitrary illumination. This problem is the motivation for the work in this chapter. Specifically, the models and algorithms presented here result from making computational efficiency the organizing pri

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Environmental Monitoring Using a Time Series of Satellite Images and Other Spatial Data Setsome parts of the state, over 20 percent of Agricultural land is no longer productive. Prior to the work to be described in this chapter, no reliable large scale estimates of the extent or progression of salinity were available. This chapter describes a methodology for monitoring the historical exten

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Traffic Flow on a Freeway Networkrs per day. Since further extensive construction of freeways is unlikely, information technology is being increasingly looked to for amelioration by providing information allowing more efficient use of existing freeways. Statistics plays a major role in such efforts.

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Internet Traffic Tends , Poisson and Independent as the Load Increasesheoretical studies of packet traffic variables — arrivals, sizes, and packet counts — demonstrate that the number of active connections has a dramatic effect on traffic characteristics. At low connection loads on an uncongested link — that is, with little or no queueing on the link-input router — th

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Cabinet

Optimal Properties and Adaptive Tuning of Standard and Nonstandard Support Vector Machinesly inside the statistical literature in decision theory and penalized likelihood regression. We review recent work on adaptive tuning of SVMs, discussing generalizations to the nonstandard case where the training set is not representative and misclassification costs are not equal. Mention is made of

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Improved Class Probability Estimates from Decision Tree Modelsrobability estimates as well. This chapter introduces a new algorithm, Bagged Lazy Option Trees (B-LOTs), for constructing decision trees and compares it to an alternative, Bagged Probability Estimation Trees (B-PETs). The quality of the class probability estimates produced by the two methods is eva

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Extended Linear Modeling with Splinesregression, logistic and Poisson regression, density estimation, spectral density estimation, and conditional density estimation. Moreover, hazard regression, proportional hazard regression, marked point process regression, and diffusion processes, all perhaps with time-dependent covariates, also fi