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Supervised Descriptive Pattern Mining

This book provides a general and comprehensible overview of supervised descriptive pattern mining, considering classic algorithms and those;

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Advances in Knowledge Discovery and Data Mining

related topics; clustering and anomaly detection; deep learning models and applications; sequential pattern mining; weakly supervised learning;

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Advances in Knowledge Discovery and Data Mining

related topics; clustering and anomaly detection; deep learning models and applications; sequential pattern mining; weakly supervised learning;

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Advances in Knowledge Discovery and Data Mining

related topics; clustering and anomaly detection; deep learning models and applications; sequential pattern mining; weakly supervised learning;

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Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

The two-volume set LNCS 8258 and 8259 constitutes the refereed proceedings of the 18th Iberoamerican Congress on Pattern Recognition, CIARP;

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Pattern Recognition

This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to;

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Preference-based Spatial Co-location Pattern Mining

mining refers to mining constrained or condensed co-location patterns instead of mining all prevalent co-location patterns. Based on the authors;

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Robust Representation for Data Analytics

unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations;

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Machine Learning and Data Mining in Pattern Recognition

This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Data Mining in Pattern;

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Machine Learning and Data Mining in Pattern Recognition

This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern;

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Machine Learning and Data Mining in Pattern Recognition

This book constitutes the refereed proceedings of the 12th International Conference on Machine Learning and Data Mining in Pattern;

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Machine Learning and Data Mining in Pattern Recognition

This book constitutes the refereed proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern;

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Machine Learning and Data Mining in Pattern Recognition

and Data Mining in Pattern Recognition, MLDM 2018, held in New York, NY, USA in July 2018. The 92 regular papers presented in this two;

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Supervised and Unsupervised Pattern Recognition

and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel;

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Machine Learning and Data Mining in Pattern Recognition

classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image;

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Semi-Supervised and Unsupervised Machine Learning

This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised;

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Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories

This SpringerBrief provides an overview within data mining of spatiotemporal frequent pattern mining from evolving regions to the;

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Machine Learning and Data Mining in Pattern Recognition

This book constitutes the refereed proceedings of the 7th International Conference on Machine Learning and Data Mining in Pattern;

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Pattern Mining with Evolutionary Algorithms

This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal;

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Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

This book constitutes the refereed conference proceedings of the 24rd Iberoamerican Congress on Pattern Recognition, CIARP 2019, held;

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Structural, Syntactic, and Statistical Pattern Recognition

This book constitutes the proceedings of the Joint IAPR International Workshop on Structural Syntactic, and Statistical Pattern Recognition;

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Sequence Data Mining

thorough coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. It offers;

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Knowledge Discovery in Multiple Databases

for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local;

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Introduction to Semi-Supervised Learning

design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining;

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Pattern Recognition Algorithms for Data Mining

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both;

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Pattern Recognition Algorithms for Data Mining

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both;

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Supervised and Unsupervised Learning for Data Science

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope;

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