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Books on the topic 'Classification used machine learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Shuurmans, Dale Eric. Effective classification learning. University of Toronto, 1996.

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2

Buntine, Wray. Myths and legends in learning classification rules. NASA, Ames Research Center, Research Institute for Advanced Computer Science, 1990.

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3

1967-, Meira Wagner, ed. Demand-driven associative classification. Springer, 2011.

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4

Suthaharan, Shan. Machine Learning Models and Algorithms for Big Data Classification. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3.

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5

Mohak, Shah, ed. Evaluating Learning Algorithms: A classification perspective. Cambridge University Press, 2011.

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6

Pattern classification using ensemble methods. World Scientific, 2010.

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7

A, Kulikowski Casimir, ed. Computer systems that learn: Classification and prediction methods from statistics, neural nets, machine learning, and expert systems. M. Kaufmann Publishers, 1991.

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8

Pham, Thuy T. Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-98675-3.

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9

Quiñonero-Candela, Joaquin, Ido Dagan, Bernardo Magnini, and Florence d’Alché-Buc, eds. Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11736790.

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10

Bacon, Simon. Machine learning for text classification of USENET newsgroups: A comparison of learning algorithms and dimensionality reduction techniques. The Author], 1997.

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11

Joaquin, Quiñonero-Candela, ed. Machine learning challenges: Evaluating predictive uncertainty visual object classification and recognizing textual entailment : First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005 : revised selected papers. Springer, 2006.

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12

Support vector machines for pattern classification. 2nd ed. Springer, 2010.

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13

Jahrestagung, Gesellschaft für Klassifikation. Data analysis, machine learning and applications: Proceedings of the 31st Annual Conference of the Gesellschaft fü̈r Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7-9, 2007. Edited by Preisach Christine. Springer, 2008.

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14

Baram, Yoram. Estimation and classification by sigmoids based on mutual information. National Aeronautics and Space Administration, 1994.

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15

Yudaev, Vasiliy. Hydraulics. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/996354.

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The textbook corresponds to the general education programs of the general courses "Hydraulics" and "Fluid Mechanics". The basic physical properties of liquids, gases, and their mixtures, including the quantum nature of viscosity in a liquid, are described; the laws of hydrostatics, their observation in natural phenomena, and their application in engineering are described. The fundamentals of the kinematics and dynamics of an incompressible fluid are given; original examples of the application of the Bernoulli equation are given. The modes of fluid motion are supplemented by the features of the

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16

Machine learning, neural and statistical classification. Ellis Horwood, 1994.

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17

Machine learning, neural and statistical classification. Prentice Hall, 1994.

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18

RAFTER, H. SUPERVISED LEARNING TECHNIQUES in MACHINE LEARNING: CLASSIFICATION. Examples with SAS. Independently Published, 2020.

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19

Ames Research Center. Artificial Intelligence Research Branch., ed. Myths and legends in learning classification rules. National Aeronautics and Space Administration, Ames Research Center, Artificial Intelligence Research Branch, 1990.

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20

Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification. Taylor & Francis Group, 2020.

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21

Kumar, Anil, A. Senthil Kumar, and Priyadarshi Upadhyay. Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification. Taylor & Francis Group, 2020.

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22

Kumar, Anil, A. Senthil Kumar, and Priyadarshi Upadhyay. Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification. Taylor & Francis Group, 2020.

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23

Kumar, Anil, A. Senthil Kumar, and Priyadarshi Upadhyay. Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification. Taylor & Francis Group, 2020.

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24

Kumar, Anil, A. Senthil Kumar, and Priyadarshi Upadhyay. Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification. Taylor & Francis Group, 2020.

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25

Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press, 2014.

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26

Alfaro, Esteban, Mat�as G�mez, and Noelia Garc�a. Ensemble Classification Methods with Applications in R. Wiley & Sons, Incorporated, John, 2018.

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27

Alfaro, Esteban, Mat�as G�mez, and Noelia Garc�a. Ensemble Classification Methods with Applications in R. Wiley & Sons, Incorporated, John, 2018.

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28

Gao, Honghao, Ying Li, Zijian Zhang, and Wenbing Zhao, eds. Machine Learning Used in Biomedical Computing and Intelligence Healthcare, Volume I. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88966-932-5.

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29

Das, Rik. Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques. Taylor & Francis Group, 2020.

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30

Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques. Taylor & Francis Group, 2020.

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31

Langford, Bill T. Classification context in a machine learning approach to predicting protein secondary structure. 1993.

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32

Das, Rik. Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques. Taylor & Francis Group, 2020.

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33

Das, Rik. Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques. Taylor & Francis Group, 2020.

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34

(Editor), Joaquin Quinonero-Candela, Ido Dagan (Editor), Bernardo Magnini (Editor), and Florence d'Alché-Buc (Editor), eds. Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First Pascal Machine ... Papers (Lecture Notes in Computer Science). Springer, 2006.

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35

Pham, Thuy T. Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings. Springer, 2019.

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36

Pham, Thuy T. Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings. Springer, 2018.

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37

Fletcher, Justin Barrows Swore. A constructive approach to hybrid architectures for machine learning. 1994.

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38

Gartner, Daniel. Optimizing Hospital-wide Patient Scheduling: Early Classification of Diagnosis-related Groups Through Machine Learning. Springer, 2015.

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39

Dowd, Cate. Digital Journalism, Drones, and Automation. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190655860.001.0001.

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Advances in online technology and news systems, such as automated reasoning across digital resources and connectivity to cloud servers for storage and software, have changed digital journalism production and publishing methods. Integrated media systems used by editors are also conduits to search systems and social media, but the lure of big data and rise in fake news have fragmented some layers of journalism, alongside investments in analytics and a shift in the loci for verification. Data has generated new roles to exploit data insights and machine learning methods, but access to big data and

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40

Suthaharan, Shan. Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning (Integrated Series in Information Systems). Springer, 2015.

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41

Artifical Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Wiley & Sons, Limited, John, 2019.

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42

Abe, Shigeo. Support Vector Machines for Pattern Classification (Advances in Pattern Recognition). Springer, 2005.

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43

Bruno, Michael A. Error and Uncertainty in Diagnostic Radiology. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190665395.001.0001.

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Diagnostic radiology is a medical specialty that is primarily devoted to the diagnostic process, centered on the interpretation of medical images. This book reviews the high level of uncertainty inherent to radiological interpretation and the overlap that exists between the uncertainty of the process and what might be considered “error.” There is also a great deal of variability inherent in the physical and technological aspects of the imaging process itself. The information in diagnostic images is subtly encoded, with a broad range of “normal” that usually overlaps the even broader range of “

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44

Whitenack, Daniel. Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language. Packt Publishing - ebooks Account, 2017.

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45

Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence (Natural Computing Series). Springer, 2007.

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46

Prasad, Girijesh. Brain–machine interfaces. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0049.

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A brain–machine interface (BMI) is a biohybrid system intended as an alternative communication channel for people suffering from severe motor impairments. A BMI can involve either invasively implanted electrodes or non-invasive imaging systems. The focus in this chapter is on non-invasive approaches; EEG-based BMI is the most widely investigated. Event-related de-synchronization/ synchronization (ERD/ERS) of sensorimotor rhythms (SMRs), P300, and steady-state visual evoked potential (SSVEP) are the three main cortical activation patterns used for designing an EEG-based BMI. A BMI involves mult

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47

Mobile Context Awareness. Springer, 2012.

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48

Herreros, Ivan. Learning and control. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0026.

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This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant cond

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49

Makatjane, Katleho, and Roscoe van Wyk. Identifying structural changes in the exchange rates of South Africa as a regime-switching process. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/919-8.

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Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The

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50

Gries, Stefan Th. Data in Construction Grammar. Edited by Thomas Hoffmann and Graeme Trousdale. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780195396683.013.0006.

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This chapter examines the types of data used in constructionist approaches and the parameters along which data types can be classified. It discusses different kinds of quantitative observational/corpus data (frequencies, probabilities, association measures) and their statistical analysis. In addition, it provides a survey of a variety of different experimental data (novel word/construction learning, priming, sorting, etc.). Finally, the chapter discusses computational-linguistic/machine-learning methods as well as new directions for the development of new data and methods in Construction Gramm

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