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Machine Learning and Knowledge Extraction

Learning and Knowledge Extraction, CD-MAKE 2017, held in Reggio, Italy, in August/September 2017. The 24 revised full papers presented were;

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Statistical Modelling and Machine Learning Principles for Bioinformatics Techniq

of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be;

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Machine Learning Theoretical Foundations and Practical Applications

algorithms and applications, feature extraction and classification, theories and models for plausible reasoning, computational learning theory;

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Machine Learning and Knowledge Extraction

Learning and Knowledge Extraction, CD-MAKE 2019, held in Canterbury, UK, in August 2019.The 25 revised full papers presented were carefully;

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Machine Learning and Systems Engineering

A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October;

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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical;

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Mastering Azure Machine Learning

experimentation, data preparation, and feature engineering using Azure Machine Learning and Python. You'll learn advanced feature extraction techniques;

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Towards Integrative Machine Learning and Knowledge Extraction

in Banff, Canada, was dedicated to stimulating a cross-domain integrative machine-learning approach and appraisal of hot topics toward tackling;

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Algorithms in Machine Learning Paradigms

, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence;

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Algorithms in Machine Learning Paradigms

, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence;

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Machine Learning for Protein Subcellular Localization Prediction

strategies for virus, plant, and eukaryote species. Three machine learning tools are introduced to improve classification refinement, feature;

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Machine Learning and Knowledge Discovery in Databases

meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and;

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Machine Learning and Knowledge Discovery in Databases

meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and;

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Machine Learning and Knowledge Discovery in Databases

learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural;

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Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing

technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and;

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Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing

promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast;

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Practical Machine Learning for Data Analysis Using Python

learning techniques Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to;

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Python Feature Engineering Cookbook

Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries;

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Thoughtful Machine Learning

Learn how to apply test-driven development (TDD) to machine-learning algorithms - and catch mistakes that could sink your analysis. In this;

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Condition Monitoring with Vibration Signals

various feature extraction, feature selection, and classification methods as well as their applications to machine vibration datasets. It also;

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Machine Learning for Text

intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning;

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Web and Big Data

; Data Management; Topic Model and Language Model Learning; Text Analysis; Text Classification; Machine Learning; Knowledge Graph; Emerging Data;

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MATLAB for Machine Learning

Extract patterns and knowledge from your data in easy way using MATLAB About This Book * Get your first steps into machine learning;

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Knowledge Acquisition and Machine Learning

For graduate-/research- level students and professors, this book integrates machine learning with knowledge acquisition to overcome the;

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Hands-On Machine Learning with IBM Watson

cognitive insights within applications Understand the role of data representation and feature extraction in any machine learning system Book;

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Information and Recommender Systems

where to find them. Computer techniques exist to facilitate this research and allow relevant information extraction. Recommendation systems;

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Machine Learning with Spark -

utilizing well-known machine learning models * Deal with large-scale text data, including feature extraction and using text data as input to your;

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