Bücher zum Thema „Self-supervised learning (artificial intelligence)“

Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

Kanerva, Pentti. The organization of an autonomous learning system. Moffett Field, CA: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1988.

Ekici, Berk. Towards self-sufficient high-rises: Performance optimisation using artificial intelligence. Delft: BK Books, 2022.

He, Haibo. Self-adaptive systems for machine intelligence. Hoboken, N.J: Wiley-Interscience, 2011.

Najim, K. Learning automata: Theory and applications. Oxford, OX, U.K: Pergamon, 1994.

Wang, Huaiqing. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Hershey PA: Engineering Science Reference, 2010.

Zhou, Zude. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Hershey PA: Engineering Science Reference, 2010.

Zhou, Zude. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Hershey PA: Engineering Science Reference, 2010.

Zhou, Zude. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Hershey, PA: Engineering Science Reference, 2010.

Klimenko, A. V. Osnovy estestvennogo intellekta: Rekurrentnai͡a︡ teorii͡a︡ samoorganizat͡s︡ii : versii͡a︡ 3. Rostov-na-Donu: Izd-vo Rostovskogo universiteta, 1994.

Friedman, Jerome. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, NY: Springer-Verlag New York, 2009.

Song, In-sŏp. Waildŭ: Song In-sŏp Kyosu ŭi A·I sidae kamsŏng ch'angjo kyoyukpŏp = Wild. 8. Aufl. Kyŏnggi-do P'aju-si: Tasan Edyu, 2020.

Hastie, Trevor. The elements of statistical learning: Data mining, inference, and prediction. New York: Springer, 2001.

Heuer, Herbert. Motor Behavior: Programming, Control, and Acquisition. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985.

Hastie, Trevor. The elements of statistical learning: Data mining, inference, and prediction : with 200 full-color illustrations. New York: Springer, 2001.

Acuña, Ana Isabel González. Contributions to unsupervised and supervised learning with applications in digital image processing: Dissertation presented to the Department Of Computer Science and Artificial Intelligence in partial fulfillment of the requeriments for the degree of Doctor of Philosophy. [Lejona, Vizcaya]: Universidad del País Vasco, Servicio Editorial = Euskal Herriko Unibertsitatea, Argitalpen Zerbitzua, 2012.

Partially Supervised Learning. Springer-Verlag Berlin and Heidelberg GmbH &, 2012.

Goldberg, Andrew, und Xiaojin Zhu. Introduction to Semi-supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning). Morgan & Claypool Publishers, 2008.

Bateman, Blaine, Benjamin Johnston, Ishita Mathur und Ashish Ranjan Jha. the Supervised Learning Workshop: A New, Interactive Approach to Understanding Supervised Learning Algorithms, 2nd Edition. Packt Publishing, Limited, 2020.

Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer, 2012.

Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer Berlin / Heidelberg, 2014.

Vallor, Shannon, und George A. Bekey. Artificial Intelligence and the Ethics of Self-Learning Robots. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190652951.003.0022.

Sawarkar, Kunal, und Dheeraj Arremsetty. Deep Learning with PyTorch Lightning: Build and Train High-Performance Artificial Intelligence and Self-Supervised Models Using Python. Packt Publishing, Limited, 2021.

Applications Of Supervised And Unsupervised Ensemble Methods. Springer, 2009.

Proudfoot, Diane, und B. Jack Copeland. Artificial Intelligence. Herausgegeben von Eric Margolis, Richard Samuels und Stephen P. Stich. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195309799.013.0007.

Autonomous Learning Systems. John Wiley & Sons, 2013.

Ehrenmueller-Jensen, Markus. Self-Service AI with Power BI Desktop: Machine Learning Insights for Business. Apress L. P., 2020.

Najim, K., und A. S. Poznyak. Learning Automata: Theory and Applications. Elsevier Science & Technology Books, 2014.

Supervised and Unsupervised Ensemble Methods and Their Applications Studies in Computational Intelligence. Springer, 2008.

Villmann, Thomas, Frank-Michael Schleif, Marika Kaden und Mandy Lange. Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4 2014. Springer London, Limited, 2014.

Eliot, Lance. Improving AI Self-Driving Cars: Practical Advances in Artificial Intelligence and Machine Learning. LBE Press Publishing, 2021.

Eliot, Lance. Revelatory AI Self-Driving Cars: Practical Advances in Artificial Intelligence and Machine Learning. LBE Press Publishing, 2021.

Eliot, Lance. AI Self-Driving Cars Imminence: Practical Advances in Artificial Intelligence and Machine Learning. LBE Press Publishing, 2021.

Eliot, Lance. Edifying AI Self-Driving Cars: Practical Advances in Artificial Intelligence and Machine Learning. LBE Press Publishing, 2021.

Villmann, Thomas, Frank-Michael Schleif, Marika Kaden und Mandy Lange. Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, ... in Intelligent Systems and Computing). Springer, 2014.

Lanham, Micheal. Hands-On Reinforcement Learning for Games: Implementing Self-Learning Agents in Games Using Artificial Intelligence Techniques. Packt Publishing, Limited, 2020.

Dutta, Sayon. Reinforcement Learning with TensorFlow: A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym. Packt Publishing, 2018.

Angelov, Plamen. Autonomous Learning Systems: From Data Streams to Knowledge in Real-Time. Wiley & Sons, Limited, John, 2012.

Angelov, Plamen. Autonomous Learning Systems: From Data Streams to Knowledge in Real-Time. Wiley & Sons, Incorporated, John, 2012.

Angelov, Plamen. Autonomous Learning Systems: From Data Streams to Knowledge in Real-Time. Wiley & Sons, Incorporated, John, 2012.

Angelov, Plamen. Autonomous Learning Systems: From Data Streams to Knowledge in Real-Time. Wiley & Sons, Incorporated, John, 2012.

Geraci, Robert M. Futures of Artificial Intelligence. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9788194831679.001.0001.

Eliot, Dr Lance. Transformative Artificial Intelligence Driverless Self-Driving Cars: Practical Advances in AI and Machine Learning. LBE Press Publishing, 2018.

Menshawy, Ahmed. Deep Learning By Example: A hands-on guide to implementing advanced machine learning algorithms and neural networks. Packt Publishing - ebooks Account, 2018.

Eliot, Dr Lance. Disruptive Artificial Intelligence and Driverless Self-Driving Cars: Practical Advances in Machine Learning and AI. LBE Press Publishing, 2018.

Eliot, Dr Lance. Revolutionary Innovations of AI Self-Driving Cars: Practical Advances in Artificial Intelligence and Machine Learning. LBE Press Publishing, 2018.

Wodecki, Andrzej. Artificial Intelligence in Management: Self-Learning and Autonomous Systems As Key Drivers of Value Creation. Elgar Publishing Limited, Edward, 2020.

Zhou, Zhi-Hua, und Friedhelm Schwenker. Partially Supervised Learning: Second IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected Papers. Springer, 2013.

Zhou, Zhi-Hua, und Friedhelm Schwenker. Partially Supervised Learning: Second IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected Papers. Springer Berlin / Heidelberg, 2013.

Schwenker, Friedhelm, und Edmondo Trentin. Partially Supervised Learning: First IAPR TC3 Workshop, PSL 2011, Ulm, Germany, September 15-16, 2011, Revised Selected Papers. Springer, 2012.