By Moamar Sayed-Mouchaweh
This e-book addresses the issues of modeling, prediction, class, info realizing and processing in non-stationary and unpredictable environments. It offers significant and famous tools and techniques for the layout of platforms capable of research and to completely adapt its constitution and to regulate its parameters in line with the alterations of their environments. additionally offers the matter of studying in non-stationary environments, its pursuits, its functions and demanding situations and reviews the complementarities and the hyperlinks among the various tools and methods of studying in evolving and non-stationary environments.
Read Online or Download Learning from Data Streams in Dynamic Environments PDF
Similar intelligence & semantics books
An Introduction to Computational Learning Theory
Emphasizing problems with computational potency, Michael Kearns and Umesh Vazirani introduce a couple of relevant subject matters in computational studying idea for researchers and scholars in man made intelligence, neural networks, theoretical computing device technology, and information. Computational studying idea is a brand new and quickly increasing zone of study that examines formal versions of induction with the pursuits of learning the typical equipment underlying effective studying algorithms and picking the computational impediments to studying.
Minimum Error Entropy Classification
This booklet explains the minimal mistakes entropy (MEE) thought utilized to info category machines. Theoretical effects at the internal workings of the MEE idea, in its software to fixing quite a few type difficulties, are provided within the wider realm of possibility functionals. Researchers and practitioners additionally locate within the e-book a close presentation of useful facts classifiers utilizing MEE.
Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms
An excellent construction calls for a powerful starting place. This booklet teaches simple synthetic Intelligence algorithms resembling dimensionality, distance metrics, clustering, blunders calculation, hill mountaineering, Nelder Mead, and linear regression. those aren't simply foundational algorithms for the remainder of the sequence, yet are very valuable of their personal correct.
Advances in Personalized Web-Based Education
This ebook goals to supply vital information regarding adaptivity in computer-based and/or web-based academic platforms. with the intention to make the coed modeling procedure transparent, a literature evaluation touching on scholar modeling recommendations and ways prior to now decade is gifted in a distinct bankruptcy.
- Intelligent Learning Environments and Knowledge Acquisition in Physics
- Semantic Web and Model-Driven Engineering
- Towards a New Evolutionary Computation: Advances in the Estimation of Distribution Algorithms
- Multi-agent systems : an introduction
Extra resources for Learning from Data Streams in Dynamic Environments
Sample text
Let us suppose now that a new concept drift occurred in the conditional probability P(xjy1) of W1 as follows (see Fig. 9): x 2 W 1 ) x $ N ðM1 ¼ ð5; 6Þ, Σ 1 ¼ ð1, 1ÞÞ 22 2 Learning in Dynamic Environments 10 8 6 4 2 0 0 2 4 6 8 10 12 Fig. 9 Drift in the class W1 passing from virtual to real concept drift. When the drift in W1 becomes real, the decision boundary is adversary impacted since the patterns of W1 enter the area of W2 in the feature space. All the patterns of W1 located in the area of W2 in the feature space will be then misclassified Some of the data samples belonging to the new concept drift of W1 will occupy the zone of class W2 in the feature space.
Let us suppose now that a new concept drift occurred in the conditional probability P(xjy1) of W1 as follows (see Fig. 9): x 2 W 1 ) x $ N ðM1 ¼ ð5; 6Þ, Σ 1 ¼ ð1, 1ÞÞ 22 2 Learning in Dynamic Environments 10 8 6 4 2 0 0 2 4 6 8 10 12 Fig. 9 Drift in the class W1 passing from virtual to real concept drift. When the drift in W1 becomes real, the decision boundary is adversary impacted since the patterns of W1 enter the area of W2 in the feature space. All the patterns of W1 located in the area of W2 in the feature space will be then misclassified Some of the data samples belonging to the new concept drift of W1 will occupy the zone of class W2 in the feature space.
10a. The latter shows a pattern x belonging to W1. The classification decisions for x according to the individual learners are: L1 : yðxÞ ¼ 1; L2 : yðxÞ ¼ 2. In order to classify correctly x, L1 must have a weight greater than the one for L2. Hence, according to the weighted majority vote, the ensemble will issue the classification decision: yðxÞ ¼ 1. 48 3 Handling Concept Drift a b W1 W1 x W1 1 W2 W2 W2 1 L1 2 1 2 L2 x W1 W2 L1 2 1 2 L2 Fig. 10 Update of the individual learners’ weights in response to the occurrence of a drift based on the use of weighted majority vote Let us now suppose that a drift occurred as we can see in Fig.