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An application of machine learning and statistics to defect detection
Intelligent Data Analysis, 2001Summary: We present an application of machine learning and statistics to the problem of distinguishing between defective and non-defective industrial workpieces, where the defect takes the form of a long and thin crack on the surface of the piece.
CUCCHIARA R +3 more
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Machine and Statistical Learning
2017Databases and big data are used for constructing models to have a better understanding of the data, or to make decisions. Machine and statistical learning offer tools for this purpose. In this chapter we review some of the methods in these areas that are of relevance in this book.
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Introduction to Statistical and Machine Learning Algorithms
2014This chapter will serve as a reference for some of the most commonly used algorithms in Microsoft Azure Machine Learning. We will provide a brief introduction to algorithms such as linear and logistic regression, k-means for clustering, decision trees, decision forests (random forests, boosted decision trees, and Gemini), neural networks, support ...
Wee Hyong Tok +2 more
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Machine Learning and Statistics Overview
2011This chapter is aimed at establishing the conceptual foundation of the relevant aspects of machine learning and statistics on which the book rests. This very brief overview is in no way exhaustive. Rather, our main aim is to elucidate the relationship of these concepts to the performance evaluation of learning algorithms. The chapter is composed of two
Nathalie Japkowicz, Mohak Shah
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2019
AbstractThis chapter describes in detail how the main techniques of statistical machine learning can be constructed from the components described in earlier chapters. It presents these concepts in a way which demonstrates how these techniques can be viewed as special cases of a more general probabilistic model which we fit to some data.
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AbstractThis chapter describes in detail how the main techniques of statistical machine learning can be constructed from the components described in earlier chapters. It presents these concepts in a way which demonstrates how these techniques can be viewed as special cases of a more general probabilistic model which we fit to some data.
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Statistical thinking, machine learning
Journal of Clinical Epidemiology, 2019Iain Buchan +3 more
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Querying the Web with Statistical Machine Learning
2014The traditional means of extracting information from the Web are keyword-based search and browsing. The Semantic Web adds structured information (i.e., semantic annotations and references) supporting both activities. One of the most interesting recent developments is Linked Open Data (LOD), where information is presented in the form of facts – often ...
Volker Tresp +2 more
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Statistical Correlations and Machine Learning for Steganalysis
2005In this paper, we present a scheme for steganalysis based on statistical correlations and machine learning. In general, digital images are highly correlated in the spatial domain and the wavelet domain; hiding data in images will affect the correlations.
Andrew H. Sung +2 more
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Brief Introduction to Statistical Machine Learning
2018In this chapter, an overview of the theory of probability, statistical and machine learning is made covering the main ideas and the most popular and widely used methods in this area. As a starting point, the randomness and determinism as well as the nature of the real-world problems are discussed.
Angelov, P.P., Gu, X.
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