A Distribution Free Theory Of Nonparametric Regression

Author by : László Györfi
Languange : en
Publisher by : Springer Science & Business Media
Format Available : PDF, ePub, Mobi
Total Read : 93
Total Download : 545
File Size : 51,7 Mb
pdf pdf

Description : This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.


Nonparametric Curve Estimation

Author by : Sam Efromovich
Languange : en
Publisher by : Springer Science & Business Media
Format Available : PDF, ePub, Mobi
Total Read : 27
Total Download : 786
File Size : 42,5 Mb
pdf pdf

Description : Numerous exercises of various levels of difficulty, given at the end of each chapter, will be very useful for the instructor and for self-study."--BOOK JACKET.


Artificial Intelligence And Soft Computing

Author by : Leszek Rutkowski
Languange : en
Publisher by : Springer
Format Available : PDF, ePub, Mobi
Total Read : 85
Total Download : 597
File Size : 54,9 Mb
pdf pdf

Description : The two-volume set LNAI 10841 and LNAI 10842 constitutes the refereed proceedings of the 17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018, held in Zakopane, Poland in June 2018. The 140 revised full papers presented were carefully reviewed and selected from 242 submissions. The papers included in the first volume are organized in the following three parts: neural networks and their applications; evolutionary algorithms and their applications; and pattern classification.


Regularization Optimization Kernels And Support Vector Machines

Author by : Johan A.K. Suykens
Languange : en
Publisher by : CRC Press
Format Available : PDF, ePub, Mobi
Total Read : 38
Total Download : 137
File Size : 46,9 Mb
pdf pdf

Description : Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.


Estimation Of Quantiles In A Simulation Model Based On Artificial Neural Networks

Author by : Sevda Alaca
Languange : en
Publisher by : GRIN Verlag
Format Available : PDF, ePub, Mobi
Total Read : 66
Total Download : 224
File Size : 52,6 Mb
pdf pdf

Description : Master's Thesis from the year 2017 in the subject Mathematics - Stochastics, grade: 1,3, Technical University of Darmstadt, language: English, abstract: This thesis deals with the development of an "alpha"-quantile estimate based on a surrogate model with the use of artificial neural networks. Using artificial neural networks as an estimate is considered a nonparametric approach. The estimation of a specific quantile of a data population is a widely used statistical task and a comprehensive way to discover the true relationship among variables. It can be classified as nonparametric regression, where it is one of the standard tasks. The most common selected levels for estimation are the first, second and third quartile (25, 50 and 75 percent). The quantile level is given by "alpha". A 25 percent quantile for example has 25 percent of the data distribution below the named quantile and 75 percent of the data distribution above it. Sometimes the tail regions of a population characteristic are of interest rather than the core of the distribution. Quantile estimation is applied in many different contexts - financial economics, survival analysis and environmental modelling are only a few of them.


Amstat News

Author by :
Languange : en
Publisher by :
Format Available : PDF, ePub, Mobi
Total Read : 70
Total Download : 454
File Size : 54,9 Mb
pdf pdf

Description :


The Annals Of Statistics

Author by :
Languange : en
Publisher by :
Format Available : PDF, ePub, Mobi
Total Read : 12
Total Download : 974
File Size : 49,5 Mb
pdf pdf

Description :


Mathematical Methods Of Statistics

Author by :
Languange : en
Publisher by :
Format Available : PDF, ePub, Mobi
Total Read : 50
Total Download : 746
File Size : 47,9 Mb
pdf pdf

Description :


Learning Theory

Author by :
Languange : en
Publisher by :
Format Available : PDF, ePub, Mobi
Total Read : 44
Total Download : 924
File Size : 49,8 Mb
pdf pdf

Description :


Introduction To Empirical Processes And Semiparametric Inference

Author by : Michael R. Kosorok
Languange : en
Publisher by :
Format Available : PDF, ePub, Mobi
Total Read : 75
Total Download : 441
File Size : 46,6 Mb
pdf pdf

Description : This book provides a self-contained, linear, and unified introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. The targeted audience includes statisticians, biostatisticians, and other researchers with a background in mathematical statistics who have an interest in learning about and doing research in empirical processes and semiparametric inference but who would like to have a friendly and gradual introduction to the area. The book can be used either as a research reference or as a textbook. The level of the book is suitable for a second year graduate course in statistics or biostatistics, provided the students have had a year of graduate level mathematical statistics and a semester of probability. The book consists of three parts. The first part is a concise overview of all of the main concepts covered in the book with a minimum of technicalities. The second and third parts cover the two respective main topics of empirical processes and semiparametric inference in depth. The connections between these two topics is also demonstrated and emphasized throughout the text. Each part has a final chapter with several case studies that use concrete examples to illustrate the concepts developed so far. The last two parts also each include a chapter which covers the needed mathematical preliminaries. Each main idea is introduced with a non-technical motivation, and examples are given throughout to illustrate important concepts. Homework problems are also included at the end of each chapter to help thereader gain additional insights.