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Mark Ryan, Luca Massaron
Machine Learning for Tabular Data

From the back cover:Advanced Analytics for Business teaches you to train insightful machine learning models on common tabular data such as spreadsheets, databases, and logs. It covers classic machine lear…

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Machine Learning for Data Science Handbook

This book is a major update to the very successful first and second editions (2005 and 2010) of Data Mining and Knowledge Discovery Handbook. Since the last edition, this field has continued to evolve and…

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Guillaume Mercère
Data Driven Model Learning for Engineers

The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate th…

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Vidya Subramanian
Applied Machine Learning for Data Science Practitioners

Applied Machine Learning for Data Science Practitioners Vidya SubramanianA single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (M…

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Machine Learning and Data Mining for Sports Analytics

This book constitutes the refereed proceedings of the 11th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2024, held in Vilnius, Lithuania, on September 9, 2024.The …

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Tsutomu Sasao
Classification Functions for Machine Learning and Data Mining

This book introduces a novel perspective on machine learning, offering distinct advantages over neural network-based techniques. This approach boasts a reduced hardware requirement, lower power consumptio…

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Shirui Wang, Jiefu Chen, Xuqing Wu, Wenyi Hu
Deep Learning for Seismic Data Enhancement and Representation

Seismic imaging is a key component of subsurface exploration, and it depends on a high-quality seismic data acquisition system with effective seismic processing algorithms. Seismic data quality concerns v…

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Machine Learning Methods for Multi-Omics Data Integration

The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays.…

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Lei Zhu, Zheng Zhang, Jingjing Li
Dynamic Graph Learning for Dimension Reduction and Data Clustering

This book illustrates how to achieve effective dimension reduction and data clustering. The authors explain how to accomplish this by utilizing the advanced dynamic graph learning technique in the era of …

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Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing

Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing features recent advances in machine learning coupled with new signal processing-based methods for cardiovascular dat…

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Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis

Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis introduces the latest emerging trends and applications of deep learning in biomedical data analysis. This book delves into va…

lieferbar ab 16.6.2025.

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James
Unlocking the Power of Streaming Data: Online Learning for Diverse Applications

Data streams are de¿ned as large sequences of data, gathered from sources such as sensor networks and customer click streams, that are possibly in¿nite and temporarily ordered [7, 22]. Instances in data s…

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National Academies of Sciences Engineering and Medicine, Health And Medicine Division, Board On Health Sciences Policy, Forum on Traumatic Brain Injury
Data Integration in Learning Health Care Systems for Traumatic Brain Injury

Traumatic brain injuries (TBI) affect millions of Americans each year, causing symptoms that can be debilitating and long-lasting and resulting in physical, cognitive, emotional, and financial ramificatio…

lieferbar in mind. 4 Wochen

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Yannick Lingelbach
Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction

This work presents a data mining framework applied to industrial heattreatment (bainitization and case hardening) aiming to optimize processes and reduce costs. The framework analyses factors such as mate…

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Nicolas Privault
Discrete Stochastic Processes

This text presents selected applications of discrete-time stochastic processes that involve random interactions and algorithms, and revolve around the Markov property. It covers recurrence properties of (…

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