Autonomously evolving classifier TEDAClass
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TL;DR: A classifier named TEDAClass (Typicality and Eccentricity based Data Analytics Classifier) which is based on the recently proposed AnYa type fuzzy rule based system which is a promising addition to the traditional probability as well as to the fuzzy logic.
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About: This article is published in Information Sciences. The article was published on 20 Oct 2016. and is currently open access. The article focuses on the topics: Cluster analysis & Classifier (UML).
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![Table 1: Recognition results comparison for MNIST[24] database](/figures/table-1-recognition-results-comparison-for-mnist-24-database-2pkkly8m.webp)
Citations
Evolving Ensemble Fuzzy Classifier
TL;DR: The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams, where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.
Empirical Data Analytics
TL;DR: The typicality is considered as a fundamental quantity in the pattern analysis, which is derived directly from data and is stated in a discrete form in contrast to the traditional approach where a continuous pdf is assumed a priori and estimated from data afterward.
Ensemble of evolving data clouds and fuzzy models for weather time series prediction
TL;DR: The objective is to develop data-centric nonlinear and time-varying models to predict mean monthly temperature using TEDA, an incremental algorithm that considers the data density and scattering of clouds over the data space.
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Self-Organising Fuzzy Logic Classifier
TL;DR: By changing the level of granularity, the proposed approach can make a trade-off between performance and computational efficiency, and, thus, the classifier is able to address a wide variety of problems with specific needs.
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References
•Book
The Nature of Statistical Learning Theory
Vladimir Vapnik
- 01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
46K
•Book
Data Mining: Practical Machine Learning Tools and Techniques
Ian H. Witten,Eibe Frank,Mark Hall +2 more
- 25 Oct 1999
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
25.4K
Fuzzy identification of systems and its applications to modeling and control
T. Takagi,Michio Sugeno +1 more
- 01 Jan 1985
TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
20.1K
•Book
Neural networks for pattern recognition
Christopher M. Bishop
- 01 Jan 1995
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.
Causality: models, reasoning, and inference
TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
14.9K