{"id":15813,"date":"2026-08-10T11:58:52","date_gmt":"2026-08-10T11:58:52","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/elsevier-machine-learning-a-constraint-based-approach-2nd-edition-2023\/"},"modified":"2026-08-10T11:58:52","modified_gmt":"2026-08-10T11:58:52","slug":"elsevier-machine-learning-a-constraint-based-approach-2nd-edition-2023","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/elsevier-machine-learning-a-constraint-based-approach-2nd-edition-2023\/","title":{"rendered":"Elsevier \u2013 Machine Learning: A Constraint-Based Approach, 2nd Edition 2023"},"content":{"rendered":"
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Description<\/span><\/h2>\n

The second edition of Machine Learning: A Constraint-Based Approach provides a fresh look at the fundamental models and algorithms of machine learning, based on the unifying concept of \u201clearning from environmental constraints.\u201d The authors attempt to provide a unified framework for understanding machine learning, focusing on current topics of interest such as neural networks and kernel machines. In this approach, symbolic knowledge bases are considered as sets of constraints, and a path is outlined for deep integration of machine learning with multivalued logics, such as fuzzy systems.<\/span><\/p>\n

Special attention is paid to the topic of deep learning in this edition, as it fits well with the constraint-based approach followed in the book. The book introduces a simpler and more unified concept of regularization, which is closely related to the Parsimony Principle. This unified structure makes it easier for students and professionals at the master\u2019s level and above to understand complex concepts.<\/span><\/p>\n

Book Features<\/span><\/h3>\n