Libros sobre el tema "Machine Learning, Artificial Intelligence, Regularization Methods"

G, Carbonell Jaime, ed. Machine learning: Paradigms and methods. Cambridge, Mass: MIT Press, 1990.

Steven, Minton y Symposium on Learning Methods for Planning Systems (1991 : Stanford University), eds. Machine learning methods for planning. San Mateo, Calif: M. Kaufmann, 1993.

G, Bourbakis Nikolaos, ed. Applications of learning & planning methods. Singapore: World Scientific, 1991.

Aldrich, Chris. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. London: Springer London, 2013.

J, Smola Alexander, ed. Learning with kernels: Support vector machines, regularization, optimization, and beyond. Cambridge, Mass: MIT Press, 2002.

Chang, Victor, Harleen Kaur y Simon James Fong, eds. Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04597-4.

Baruque, Bruno. Fusion methods for unsupervised learning ensembles. Berlin: Springer, 2010.

Katharina, Morik, ed. Knowledge acquisition and machine learning: Theory, methods and applications / Katharina Morik ... [et al.]. London: Academic Press, 1993.

service), SpringerLink (Online, ed. Criminal Justice Forecasts of Risk: A Machine Learning Approach. New York, NY: Springer New York, 2012.

Léon-Charles, Tranchevent, Moor Bart, Moreau Yves y SpringerLink (Online service), eds. Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

1970-, Gonzalez Fabio A. y Romero Eduardo 1963-, eds. Biomedical image analysis and machine learning technologies: Applications and techniques. Hershey, PA: Medical Information Science Reference, 2010.

1970-, Gonzalez Fabio A. y Romero Eduardo 1963-, eds. Biomedical image analysis and machine learning technologies: Applications and techniques. Hershey, PA: Medical Information Science Reference, 2010.

Søren, Brunak, ed. Bioinformatics: The machine learning approach. 2^a ed. Cambridge, Mass: MIT Press, 2001.

Suzuki, Kenji. Machine Learning in Medical Imaging: Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

N, Vagin V., ed. Dostovernyĭ i pravdopodobnyĭ vyvod v intellektualʹnykh sistemakh. Moskva: Fizmatlit, 2004.

Topolsky, Nikolay y Valeriy Vilisov. Methods, models and algorithms in security systems: machine learning, robotics, insurance, risks, control. ru: Publishing Center RIOR, 2021. http://dx.doi.org/10.29039/02072-2.

Wang, Fei. Machine Learning in Medical Imaging: Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 1, 2012, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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

Robert, Tibshirani y Friedman J. H, eds. The elements of statistical learning: Data mining, inference, and prediction : with 200 full-color illustrations. New York: Springer, 2001.

Jens, Knoop, Margaria-Steffen Tiziana 1964-, Schreiner Dietmar, Steffen Bernhard y SpringerLink (Online service), eds. Leveraging Applications of Formal Methods, Verification, and Validation: International Workshops, SARS 2011 and MLSC 2011, Held Under the Auspices of ISoLA 2011 in Vienna, Austria, October 17-18, 2011. Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

Minton, Steven. Machine Learning Methods for Planning. Elsevier Science & Technology Books, 2014.

Bonet, Blai y Hector Geffner. Concise Introduction to Models and Methods for Automated Planning. Morgan & Claypool Publishers, 2013.

Bonet, Blai y Hector Geffner. Concise Introduction to Models and Methods for Automated Planning. Morgan & Claypool Publishers, 2013.

Carbonell, Jaime G. Machine Learning: Paradigms and Methods (Special Issues of Artificial Intelligence). The MIT Press, 1990.

Aldrich, Chris y Lidia Auret. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. Springer, 2013.

Aldrich, Chris y Lidia Auret. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. Springer, 2016.

Jabbar, Meerja Akhil, Kantipudi Mvv Prasad, Mamun Bin Ibne Reaz, Sheng-Lung Peng y Ana Maria Madureira. Machine Learning Methods for Signal, Image and Speech Processing. River Publishers, 2021.

Reaz, Mamun Bin Ibne, M. V. V. Prasad Kantipudi, Jabbar M. A, Sheng-Lung Peng y Ana Maria Madureira. Machine Learning Methods for Signal, Image and Speech Processing. River Publishers, 2022.

Ma, Xuelei, Lei Deng, Rong Tian y Chunxiao Guo, eds. Novel Methods for Oncologic Imaging Analysis: Radiomics, Machine Learning, and Artificial Intelligence. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-347-9.

Unsupervised Process Monitoring And Fault Diagnosis With Machine Learning Methods. Springer London Ltd, 2013.

Morik, Katharina, Jorg-Uwe Kietz, Stefan Wrobel y Werner Emde. Knowledge Acquisition and Machine Learning: Theory, Methods, and Applications. Elsevier Science & Technology Books, 1993.

Campedelli, Gian Maria. Machine Learning for Criminology and Criminal Research: At the Crossroads. Taylor & Francis Group, 2022.

Campedelli, Gian Maria. Machine Learning for Criminology and Criminal Research: At the Crossroads. Taylor & Francis Group, 2022.

Machine Learning for Criminology and Criminal Research: At the Crossroads. Taylor & Francis Group, 2022.

Campedelli, Gian Maria. Machine Learning for Criminology and Criminal Research: At the Crossroads. Routledge, 2022.

Bahadur, Issam Bait, Kishor Kumar Sadasivuni, Sumaya Al-Maadeed, Huseyin C. Yalcin y Hassen M. Ouakad. Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods. Wiley & Sons, Incorporated, John, 2022.

Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods. Wiley & Sons, Limited, John, 2022.

Bahadur, Issam Bait, Kishor Kumar Sadasivuni, Sumaya Al-Maadeed, Huseyin C. Yalcin y Hassen M. Ouakad. Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods. Wiley & Sons, Incorporated, John, 2022.

Bahadur, Issam Bait, Kishor Kumar Sadasivuni, Sumaya Al-Maadeed, Huseyin C. Yalcin y Hassen M. Ouakad. Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods. Wiley & Sons, Incorporated, John, 2022.

Chang, Victor, Harleen Kaur y Simon James Fong. Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases. Springer International Publishing AG, 2022.

Baruque, Bruno. Fusion Methods for Unsupervised Learning Ensembles. Springer, 2014.

Baruque, Bruno. Fusion Methods for Unsupervised Learning Ensembles. Springer, 2010.

Statistical Reinforcement Learning. CRC Press, 2012.

Morik, Katharina, Jorg-Uwe Kietz, Stefan Wrobel y Werner Emde. Knowledge Acquisition and Machine Learning: Theory, Methods, and Applications (Knowledge-Based Systems). Academic Press, 1993.

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

Berk, Richard. Criminal Justice Forecasts of Risk: A Machine Learning Approach. Springer, 2012.

Tranchevent, Léon-Charles, Bart Moor, Shi Yu y Yves Moreau. Kernel-Based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining. Springer Berlin / Heidelberg, 2013.

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

Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more. Packt Publishing, 2018.

Suzuki, Kenji, Fei Wang, Daoqiang Zhang, Guorong Wu, Dinggang Shen y Pingkun Yan. Machine Learning in Medical Imaging: 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, ... Springer, 2013.