Books on the topic 'Representation learning (artifical intelligence)'
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Consult the top 38 books for your research on the topic 'Representation learning (artifical intelligence).'
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Pacific, Rim International Conference on Artificial Intelligence (4th 1996 Cairns Qld ). PRICAI '96: Topics in artificial intelligence : 4th Pacific Rim International Conference on Artificial Intelligence, Cairns, Australia, August 26-30, 1996 : proceedings. Berlin: Springer, 1996.
Brighton, England) International Conference on Artificial Intelligence in Education (14th 2009. Artificial intelligence in education: Building learning systems that care : from knowledge representation to affective modelling. Amsterdam: IOS Press, 2009.
1953-, Benjamin D. Paul, ed. Change of representation and inductive bias. Boston: Kluwer Academic, 1990.
Andrée, Tiberghien, Mandl Heinz, and NATO Advanced Research Workshop on Knowledge Acquisition in the Domain of Physics and Intelligent Learning Environments (1990 : Lyon, France), eds. Intelligent learning environments and knowledge acquisition in physics. Berlin: Springer-Verlag, 1992.
Tiberghien, Andrée. Intelligent Learning Environments and Knowledge Acquisition in Physics. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992.
Workshop on Reasoning with Incomplete and Changing Information (1996 Cairns, Qld.). Learning and reasoning with complex representations: PRICAI'96 Workshops on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations, Cairns, Australia, August 26-30, 1996 : selected papers. Berlin: Springer, 1998.
Fisseler, Jens. Learning and modeling with probabilistic conditional logic. Heidelberg: Ios Press, 2010.
KR4HC 2009 (2009 Verona, Italy). Knowledge representation for health-care: Data, processes and guidelines : AIME 2009 workshop KR4HC 2009, Verona, Italy, July 19, 2009 : revised selected papers. Berlin: Springer, 2010.
Riaño, David. Knowledge Representation for Health-Care: ECAI 2010 Workshop KR4HC 2010, Lisbon, Portugal, August 17, 2010, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
United States. National Aeronautics and Space Administration., ed. Instructable autonomous agents: CSE-TR-193-94. [Washington, DC: National Aeronautics and Space Administration, 1994.
Bareiss, Ray. Exemplar-based knowledge acquisition: A unified approach to concept representation, classification, and learning. Boston: Academic Press, 1989.
Lin, Yankai, Zhiyuan Liu, and Maosong Sun. Representation Learning for Natural Language Processing. Springer Singapore Pte. Limited, 2020.
Liu, Zhiyuan. Representation Learning for Natural Language Processing. Springer Nature, 2020.
Lin, Yankai, Zhiyuan Liu, and Maosong Sun. Representation Learning for Natural Language Processing. Springer Singapore Pte. Limited, 2020.
Di Maio, Paolad. Knowledge Representation for Debiasing. MTS Press, 2021. http://dx.doi.org/10.52844/slkrns1.
(Editor), Norman Foo, and Randy Goebel (Editor), eds. Pricai '96: Topics in Artificial Intelligence : 4th Pacific Rim International Conference on Artificial Intelligence, Cairns, Australia, August 26-30, 1996 : Proce (Lecture Notes in Computer Science). Springer, 1996.
Gou, Jianping, Weihua Ou, Shaoning Zeng, and Lan Du, eds. Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics. MDPI, 2023. http://dx.doi.org/10.3390/books978-3-0365-7263-5.
Benjamin, D. Paul. Change of Representation and Inductive Bias. Springer, 2011.
Boden, Margaret A. 2. General intelligence as the Holy Grail. Oxford University Press, 2018. http://dx.doi.org/10.1093/actrade/9780199602919.003.0002.
(Editor), Jonathan Lawry, Jimi Shanahan (Editor), and Anca Ralescu (Editor), eds. Modelling with Words: Learning, Fusion, and Reasoning within a Formal Linguistic Representation Framework (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence). Springer, 2004.
Domingos, Pedro. Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books, 2015.
Domingos, Pedro. Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Penguin Books, Limited, 2015.
Domingos, Pedro. Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Penguin Books, Limited, 2017.
Domingos, Pedro. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books, 2018.
Domingos, Pedro. The master algorithm: How the quest for the ultimate learning machine will remake our world. 2015.
L'algoritmo definitivo: La macchina che impara da sola e il futuro del nostro mondo. Bollati Boringhieri, 2016.
Domingos, Pedro. Verkhovnyĭ algoritm: Kak mashinnoe obuchenie izmenit nash mir. 2016.
Jackson, Stuart A. Connectionism and Vermont: From Truth Conditions to Weight Representations (Albex Series in Artificial Intelligence). Ablex Publishing Corporation, 1997.
Jackson, Stuart A. Connectionism and Meaning: From Truth Conditions to Weight Representations (Ablex Series in Artificial Intelligence). Ablex Publishing Corporation, 1997.
(Editor), Ulrich Hoppe, Felisa Verdejo (Editor), and Judy Kay (Editor), eds. Artifical Intelligence in Education: Shaping the Future of Learning Through Intelligent Technologies (Frontiers in Artificial Intelligence and Applications,). IOS Press, 2003.
Ward, Matt, and Bernard Marr. Artifical Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Wiley & Sons, Limited, John, 2019.
Caselli, Tommaso, Eduard Hovy, Martha Palmer, and Piek Vossen, eds. Computational Analysis of Storylines. Cambridge University Press, 2021. http://dx.doi.org/10.1017/9781108854221.
Leondes, Cornelius T. Knowledge-Based Systems Techniques and Applications (4-Volume Set). Academic Press, 2000.
Leondes, Cornelius T. Knowledge-Based Systems Techniques and Applications (4-Volume Set). Academic Press, 2000.
Bareiss, Ray, and B. Chandrasekaran. Exemplar-Based Knowledge Acquisition: A Unified Approach to Concept Representation, Classification, and Learning. Elsevier Science & Technology Books, 2014.
The Expected Knowledge: What can we know about anything and everything? Tiruchirappalli: Sivashanmugam Palaniappan, 2012.
Bareiss, Ray. Exemplar Based Knowledge Acquisition: A Unified Approach to Concept Representation Classification, and Learning (Prspctvs in Art Intlgnce, Vol 2). Academic Pr, 1990.
Bareiss, Ray. Exemplar Based Knowledge Acquisition: A Unified Approach to Concept Representation Classification, and Learning (Prspctvs in Art Intlgnce, Vol 2). Academic Pr, 1990.