By Joaquim P. Marques de Sá, Luís M.A. Silva, Jorge M.F. Santos, Luís A. Alexandre
This booklet explains the minimal errors entropy (MEE) suggestion utilized to facts type machines. Theoretical effects at the internal workings of the MEE thought, in its program to fixing various category difficulties, are offered within the wider realm of chance functionals.
Researchers and practitioners additionally locate within the ebook a close presentation of sensible info classifiers utilizing MEE. those comprise multi‐layer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and determination timber. A clustering set of rules utilizing a MEE‐like notion can also be offered. Examples, exams, evaluate experiments and comparability with related machines utilizing vintage methods, supplement the descriptions.
Read or Download Minimum Error Entropy Classification PDF
Similar intelligence & semantics books
An Introduction to Computational Learning Theory
Emphasizing problems with computational potency, Michael Kearns and Umesh Vazirani introduce a few significant subject matters in computational studying conception for researchers and scholars in man made intelligence, neural networks, theoretical machine technology, and statistics. Computational studying conception is a brand new and speedily increasing region of analysis that examines formal versions of induction with the ambitions of studying the typical tools underlying effective studying algorithms and determining the computational impediments to studying.
Minimum Error Entropy Classification
This ebook explains the minimal blunders entropy (MEE) inspiration utilized to facts category machines. Theoretical effects at the internal workings of the MEE thought, in its software to fixing a number of type difficulties, are awarded within the wider realm of hazard functionals. Researchers and practitioners additionally locate within the ebook an in depth presentation of useful facts classifiers utilizing MEE.
Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms
An outstanding construction calls for a robust origin. This publication teaches easy synthetic Intelligence algorithms equivalent to dimensionality, distance metrics, clustering, mistakes calculation, hill mountain climbing, Nelder Mead, and linear regression. those usually are not simply foundational algorithms for the remainder of the sequence, yet are very beneficial of their personal correct.
Advances in Personalized Web-Based Education
This ebook goals to supply very important information regarding adaptivity in computer-based and/or web-based academic structures. so as to make the scholar modeling procedure transparent, a literature overview referring to scholar modeling innovations and techniques in past times decade is gifted in a distinct bankruptcy.
- Readings in fuzzy sets for intelligent systems
- Language processing in social context
- Ingenieur Analysis 1
- Readings in fuzzy sets for intelligent systems
Additional info for Minimum Error Entropy Classification
Sample text
05[×[0, 1]. In Fig. 1a the data consists of 600 instances per class and the MMSE regression solution results indeed in one of the Pˆe = 0 straight lines. This is the large size case; for large n (say, n > 400 instances per class) one obtains solutions with no misclassified instances, practically always. , with practically no misclassified instances) to largely deviated as in Fig. 1b, exhibiting a substantial number of misclassified instances. Finally, in Fig. 1c, the same dataset as in Fig. 05 added to component x2 of class 1 (’crosses’); this small "noise" value was enough to provoke a substantial departure from a fw∗ solution, in spite of the fact that the data is still linearly separable.
Is it possible to conceive data classification problems where MCE and MEE perform better than MMSE? And where MEE outperforms both MCE and MMSE? The answer to these questions is affirmative, as we shall now show with a simple example of a family of data classification problems, where for an infinite subset of the family MEE provides the correct solution, whereas MMSE and MCE do not [150, 219]. 7. Let us consider a family of two-class datasets in bivariate space R2 , target space T = {−1, 1}. 50) 2 2 where u(x; a, b) is the uniform density in [a, b].
2a. 01 1 produces the evolution shown by Figs. 2d. 9997). A small departure from the asymptotic solution means in 1 The value of η influences the convergence rate. 01 value was chosen so that a convenient number of illustrative intermediary PDFs were obtained. 5 0 −4 0 e e −2 0 (c) 2 4 0 −4 −2 0 2 4 (d) Fig. 1; (b), (c) and (d), show the PDF at iterations 28, 30, and 31, respectively. this case Pe = 0. 5! 32). In short, minimization of the error entropy behaves poorly in this case. 1 illustrates the minimization of theoretical error entropy leading to very different results, depending on the initial choice of parameter vectors.