Variable selection in wavelet regression models

TL;DR: A review of available methods for variable selection within one of the many modeling approaches for high-throughput data, Partial Least Squares Regression, to get an understanding of the characteristics of the methods and to get a basis for selecting an appropriate method for own use.

TL;DR: In this paper, the authors proposed a variable selection strategy for multivariate calibration using the Successive Projections Algorithm, a forward selection method which uses simple operations in a vector space to minimize variable collinearity.

TL;DR: A computational framework is presented that may help in the understanding of the nature of grounded representations and two models are briefly introduced that aim at emphasizing the difference the authors make between implicit and explicit representations.

TL;DR: It is concluded that WDDFF is a useful tool for forecasting real-world hydrological and water resources processes that overcomes the limitations of many earlier wavelet-based forecasting methods and should be explored further for forecasting different processes such as streamflow, rainfall, evaporation, etc.

TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.

TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.

TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.

TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.