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Books on the topic 'Machine learning interactif'

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Published: 14 December 2024

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1

Renals, Steve, Samy Bengio, and Jonathan G. Fiscus, eds. Machine Learning for Multimodal Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11965152.

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2

Popescu-Belis, Andrei, Steve Renals, and Hervé Bourlard, eds. Machine Learning for Multimodal Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78155-4.

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3

Bengio, Samy, and Hervé Bourlard, eds. Machine Learning for Multimodal Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b105752.

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4

Popescu-Belis, Andrei, and Rainer Stiefelhagen, eds. Machine Learning for Multimodal Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85853-9.

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5

Renals, Steve, and Samy Bengio, eds. Machine Learning for Multimodal Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11677482.

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6

Raedt, Luc de. Interactive theory revision: An inductive logic programming approach. London: Academic Press, 1992.

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7

Naidenova, Xenia. Machine learning methods for commonsense reasoning processes: Interactive models. Hershey, PA: Information Science Reference, 2010.

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8

Naidenova, Xenia. Machine learning methods for commonsense reasoning processes: Interactive models. Hershey, PA: Information Science Reference, 2010.

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9

Bösser, Tom. Learning in man-computer interaction: Areview of the literature. Berlin: Springer-Verlag, 1987.

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10

Bösser, Tom. Learning in man-computer interaction: A review of the literature. Berlin: Springer-Verlag, 1987.

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11

Sun, Mengzhen. Machine Learning Applications in Proteins: Interaction Prediction and Structure Prediction. [New York, N.Y.?]: [publisher not identified], 2021.

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12

International Symposium on the Occasion of the 25th Anniversary of the McGill University Centre for Intelligent Machines. Brain, body and machine: Proceedings of an International Symposium on the Occasion of the 25th Anniversary of McGill University Centre for Intelligent Machines. Berlin: Springer, 2010.

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13

Samy, Bengio, and Bourlard Hervé 1956-, eds. Machine learning for multimodal interaction: First international workshop, MLMI 2004, Martigny, Switzerland, June 21-23, 2004 : revised selected papers. Berlin: Springer, 2005.

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14

Cavicchio, Federica, and Emanuela Magno Caldognetto, eds. Aspetti emotivi e relazionali nell'e-learning. Florence: Firenze University Press, 2008. http://dx.doi.org/10.36253/978-88-8453-833-8.

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This book investigates the role of emotions and multimodal communication in face-to-face teaching and in e-learning, and assesses the incidence of these not merely verbal components on the cognitive processes of the student. It also presents certain types of man-machine interface that utilise natural language in written, vocal and multimodal form; the latter implement a new metaphor of interaction with the computer that is more human-oriented. This is, therefore, a new and interdisciplinary theme of research that highlights the technical and theoretical complexity that e-learning specialists and scholars of multimodal communication and emotions address in order to devise new systems of human-computer communication that are more natural and more motivating for learning.
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15

Wei, Chia-Hung. Machine learning techniques for adaptive multimedia retrieval: Technologies, applications, and perspectives. Hershey, PA: Information Science Reference, 2011.

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16

Heintz, Molly, Avinash Rajagopal, and Barbara Eldredge. Changing lives, reimagining machines, improving cities, revolutionizing industries and shaping the future right before your eyes... Edited by Uptake (Firm) and Pentagram Design. Chicago, IL: Uptake, 2017.

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17

Andrei, Popescu-Belis, Renals Steve, and Bourlard Hervé 1956-, eds. Machine learning for multimodal interaction: 4th international workshop, MLMI 2007, Brno, Czech Republic, June 28-30, 2007 : revised selected papers. Berlin: Springer, 2008.

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18

United States. National Aeronautics and Space Administration., ed. Instructable autonomous agents: CSE-TR-193-94. [Washington, DC: National Aeronautics and Space Administration, 1994.

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19

Learner/Machine Interaction in Computer-assisted Language Learning (Workshop) (1984 Lancaster). Report of the IEC/CILT Computer-Assisted Learning Workshop 'Learner/Machine Interaction in Computer-Assisted Language Learning' held at St. Martin's College, Lancaster on 18th and 19th September 1984. London: CILT, 1985.

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20

International Conference on Knowledge and Systems Engineering (2nd 2010 Hanoi, Vietnam). KSE 2010: Proceedings : Second International Conference on Knowledge and Systems Engineering : Hanoi, Vietnam, 7-9 October 2010. Edited by Pham Son Bao and Đại học quó̂c gia Hà Nội. University of Engineering and Technology. Los Alamitos, Calif: IEEE Computer Society, 2010.

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21

Polyakova, Anna, Tat'yana Sergeeva, and Irina Kitaeva. The continuous formation of the stochastic culture of schoolchildren in the context of the digital transformation of general education. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1876368.

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The material presented in the monograph shows the possibilities of continuous teaching of mathematics at school, namely, the significant potential of modern information and communication technologies, with the help of which it is possible to form elements of stochastic culture among students. Continuity in learning is considered from two positions: procedural and educational-cognitive. In addition, a distinctive feature of the book is the presentation of the digital transformation of general education as a way to overcome the "new digital divide". Methodological features of promising digital technologies (within the framework of teaching students the elements of the probabilistic and statistical line) that contribute to overcoming the "new digital divide": artificial intelligence, the Internet of Things, additive manufacturing, machine learning, blockchain, virtual and augmented reality are described. The solution of the main questions of probability theory and statistics in the 9th grade mathematics course is proposed to be carried out using a distance learning course built in the Moodle distance learning system. The content, structure and methodological features of the implementation of the stochastics course for students of grades 10-11 of a secondary school are based on the use of such tools in the educational process as an online calculator for plotting functions, the Wolfram Alpha service, Google Docs and Google Tables services, the Yaklass remote training, the Banktest website.<url>", interactive module "Galton Board", educational website "Mathematics at school". It will be interesting for students, undergraduates, postgraduates, mathematics teachers, as well as specialists improving their qualifications in the field of pedagogical education.
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22

Herv Bourlard,Steve Renals,Andrei Popescu-Belis. Machine Learning for Multimodal Interaction. Springer, 2008.

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23

Bourlard, Samy Bengio Herv. Machine Learning for Multimodal Interaction. Springer, 2008.

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24

Samy, Bengio Steve Renals. Machine Learning for Multimodal Interaction. Springer, 2008.

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25

Fiebrink, Rebecca A., and Baptiste Caramiaux. The Machine Learning Algorithm as Creative Musical Tool. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.23.

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Machine learning is the capacity of a computational system to learn structure from data in order to make predictions on new data. This chapter draws on music, machine learning, and human-computer interaction to elucidate an understanding of machine learning algorithms as creative tools for music and the sonic arts. It motivates a new understanding of learning algorithms as human-computer interfaces: like other interfaces, learning algorithms can be characterized by the ways their affordances intersect with goals of human users. The chapter also argues that the nature of interaction between users and algorithms impacts the usability and usefulness of those algorithms in profound ways. This human-centred view of machine learning motivates a concluding discussion of what it means to employ machine learning as a creative tool.
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26

Subia, Darell. Technological Era : Look at the Interaction Between Machine and Civilization: Machine Learning Course. Independently Published, 2021.

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27

(Editor), Samy Bengio, and Hervé Bourlard (Editor), eds. Machine Learning for Multimodal Interaction: First International Workshop, MLMI 2004, Martigny, Switzerland, June 21-23, 2004, Revised Selected Papers (Lecture Notes in Computer Science). Springer, 2005.

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28

Panigrahi, Lipismita, Sandeep Biswal, Akash Kumar Bhoi, Akhtar Kalam, and Paolo Barsocchi, eds. Machine Learning and AI Techniques in Interactive Medical Image Analysis. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4671-3.

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29

Biswal, Sandeep, Lipismita Panigrahi, Akhtar Kalam, Akash Kumar Bhoi, and Paolo Barsocchi. Machine Learning and AI Techniques in Interactive Medical Image Analysis. IGI Global, 2022.

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30

Biswal, Sandeep, Lipismita Panigrahi, Akhtar Kalam, Akash Kumar Bhoi, and Paolo Barsocchi. Machine Learning and AI Techniques in Interactive Medical Image Analysis. IGI Global, 2022.

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31

Biswal, Sandeep, Lipismita Panigrahi, Akhtar Kalam, Akash Kumar Bhoi, and Paolo Barsocchi. Machine Learning and AI Techniques in Interactive Medical Image Analysis. IGI Global, 2022.

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32

Biswal, Sandeep, Lipismita Panigrahi, Akhtar Kalam, Akash Kumar Bhoi, and Paolo Barsocchi. Machine Learning and AI Techniques in Interactive Medical Image Analysis. IGI Global, 2022.

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33

Biswal, Sandeep, Lipismita Panigrahi, Akhtar Kalam, Akash Kumar Bhoi, and Paolo Barsocchi. Machine Learning and AI Techniques in Interactive Medical Image Analysis. IGI Global, 2022.

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34

Human-In-the-Loop Machine Learning: Active Learning, Annotation, and Human-Computer Interaction. Manning Publications Company, 2020.

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35

Kumar, Sandeep, Rohit Raja, Shilpa Rani, and Shrikant Tiwari. Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithm. Wiley & Sons, Limited, John, 2022.

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36

Kumar, Sandeep, Rohit Raja, Shrikant Tiwari, and Shilpa Rani, eds. Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithm. Wiley, 2021. http://dx.doi.org/10.1002/9781119792109.

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37

Kumar, Sandeep, Rohit Raja, Shilpa Rani, and Shrikant Tiwari. Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms. Wiley & Sons, Incorporated, John, 2021.

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38

Roiger, Richard J. Just Enough R!: An Interactive Approach to Machine Learning and Analytics. Taylor & Francis Group, 2020.

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39

Roiger, Richard J. Just Enough R!: An Interactive Approach to Machine Learning and Analytics. Taylor & Francis Group, 2020.

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40

Kumar, Sandeep, Rohit Raja, Shilpa Rani, and Shrikant Tiwari. Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms. Wiley & Sons, Incorporated, John, 2021.

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41

Roiger, Richard J. Just Enough R!: An Interactive Approach to Machine Learning and Analytics. Taylor & Francis Group, 2020.

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42

Roiger, Richard J. Just Enough R!: An Interactive Approach to Machine Learning and Analytics. Taylor & Francis Group, 2020.

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43

Kumar, Sandeep, Rohit Raja, Shilpa Rani, and Shrikant Tiwari. Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms. Wiley & Sons, Incorporated, John, 2021.

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44

Sosnin, Petr. Experience-Based Human-Computer Interactions: Emerging Research and Opportunities. IGI Global, 2017.

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45

Muggleton, Stephen, and Nicholas Chater, eds. Human-Like Machine Intelligence. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198862536.001.0001.

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In recent years there has been increasing excitement concerning the potential of Artificial Intelligence to transform human society. This book addresses the leading edge of research in this area. The research described aims to address present incompatibilities of Human and Machine reasoning and learning approaches. According to the influential US funding agency DARPA (originator of the Internet and Self-Driving Cars) this new area represents the Third Wave of Artificial Intelligence (3AI, 2020s–2030s), and is being actively investigated in the US, Europe and China. The EPSRC’s UK network on Human-Like Computing (HLC) was one of the first internationally to initiate and support research specifically in this area. Starting activities in 2018, the network represents around sixty leading UK groups Artificial Intelligence and Cognitive Scientists involved in the development of the inter-disciplinary area of HLC. The research of network groups aims to address key unsolved problems at the interface between Psychology and Computer Science. The chapters of this book have been authored by a mixture of these UK and other international specialists based on recent workshops and discussions at the Machine Intelligence 20 and 21 workshops (2016,2019) and the Third Wave Artificial Intelligence workshop (2019). Some of the key questions addressed by the Human-Like Computing programme include how AI systems might 1) explain their decisions effectively, 2) interact with human beings in natural language, 3) learn from small numbers of examples and 4) learn with minimal supervision. Solving such fundamental problems involves new foundational research in both the Psychology of perception and interaction as well as the development of novel algorithmic approaches in Artificial Intelligence.
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46

Oulasvirta, Antti, Per Ola Kristensson, Xiaojun Bi, and Andrew Howes, eds. Computational Interaction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.001.0001.

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This book presents computational interaction as an approach to explaining and enhancing the interaction between humans and information technology. Computational interaction applies abstraction, automation, and analysis to inform our understanding of the structure of interaction and also to inform the design of the software that drives new and exciting human-computer interfaces. The methods of computational interaction allow, for example, designers to identify user interfaces that are optimal against some objective criteria. They also allow software engineers to build interactive systems that adapt their behaviour to better suit individual capacities and preferences. Embedded in an iterative design process, computational interaction has the potential to complement human strengths and provide methods for generating inspiring and elegant designs. Computational interaction does not exclude the messy and complicated behaviour of humans, rather it embraces it by, for example, using models that are sensitive to uncertainty and that capture subtle variations between individual users. It also promotes the idea that there are many aspects of interaction that can be augmented by algorithms. This book introduces computational interaction design to the reader by exploring a wide range of computational interaction techniques, strategies and methods. It explains how techniques such as optimisation, economic modelling, machine learning, control theory, formal methods, cognitive models and statistical language processing can be used to model interaction and design more expressive, efficient and versatile interaction.
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47

Luckin, Rosemary. Machine Learning and Human Intelligence: The Future of Education for the 21st Century. UCL IOE Press, 2018.

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48

Kohli, Pushmeet, Zhengyou Zhang, Ling Shao, and Jungong Han. Computer Vision and Machine Learning with RGB-D Sensors. Springer, 2014.

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49

Kohli, Pushmeet, Zhengyou Zhang, Ling Shao, and Jungong Han. Computer Vision and Machine Learning with RGB-D Sensors. Springer, 2016.

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50

Computer Vision and Machine Learning with RGB-D Sensors. Springer, 2014.

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