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This compendium provides a self-contained introduction to mathematical analysis in the field of machine learning and data mining. The;
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for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and;
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With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role;
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learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching;
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such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including results. Key;
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analysis.* An excellent way to introduce students to especially vital applications of data mining and machine learning technology.;
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recommendation, intelligent interaction support for e-learning, data mining for instructional design, learning and assessment, students' access patterns;
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, Machine Learning, and Big Data - That You Don't Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing;
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be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to;
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, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications;
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Information and computer technologies for data analysis and processing in various fields of data mining and machine learning generates the;
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learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific;
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The 5th Symposium on Data Mining Applications (SDMA 2018) provides valuable opportunities for technical collaboration among data mining and;
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This book constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining;
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for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and;
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organized in topical sections named: Data Mining: Natural Language Processing and Text Mining; Image Analysis and Retrieval; Machine Learning and;
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analysis; spatio-temporal data; natural language processing and text mining; data mining applications; applications; and demos.;
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Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds;
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Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds;
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This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Data Mining in Pattern;
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This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern;
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This book constitutes the refereed proceedings of the 12th International Conference on Machine Learning and Data Mining in Pattern;
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This book constitutes the refereed proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern;
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for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.;
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This book constitutes the refereed post-conference proceedings of the 7th International Workshop on Machine Learning and Data Mining for;
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