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Machine Learning and Information Processing

This book includes selected papers from the International Conference on Machine Learning and Information Processing (ICMLIP 2019), held at;

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Machine Learning and Information Processing

This book includes selected papers from the 2nd International Conference on Machine Learning and Information Processing (ICMLIP 2020), held;

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Signal Processing and Machine Learning with Applications

processes are among the signal processing languages we use to interpret real-world phenomena, model them, and extract useful information. This book;

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Quantum Machine Learning

for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum;

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Pattern Recognition and Machine Intelligence

reviewed and selected from 341 submissions. They are organized in topical sections named: Pattern Recognition; Machine Learning; Deep Learning;

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Pattern Recognition and Machine Intelligence

reviewed and selected from 341 submissions. They are organized in topical sections named: Pattern Recognition; Machine Learning; Deep Learning;

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Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth;

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Information Management and Machine Intelligence

of topics, including data analytics; AI; machine and deep learning; information management, security, processing techniques and interpretation;

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Signal Processing and Machine Learning for Biomedical Big Data

information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such;

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Financial Signal Processing and Machine Learning

market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and;

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Lecture Notes in Computational Intelligence and Decision Making

Information and computer technologies for data analysis and processing in various fields of data mining and machine learning generates the;

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Document Processing Using Machine Learning

Document Processing Using Machine Learning aims at presenting a handful of resources for students and researchers working in the document;

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Practical Machine Learning and Image Processing

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins;

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Practical Machine Learning and Image Processing

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins;

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Machine Learning with PySpark

Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business;

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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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Cooperative and Graph Signal Processing

, inference and machine learning. Building on the principles of these areas, the book then shows how they are relevant to understanding distributed;

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Intelligent Information Processing IX

submissions. They are organized in topical sections on machine learning, deep learning, multi-agent systems, neural computing and swarm intelligence;

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Proceedings of the Second International Conference on Information Management and

covers a range of topics, including data analytics; AI; machine and deep learning; information management, security, processing techniques and;

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Intelligent Systems in Big Data, Semantic Web and Machine Learning

based on relevant conceptual and formal development. The coverage is wide, ranging from machine learning to the use of data on the Semantic Web;

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Neural Information Processing Part I

Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and;

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Neural Information Processing Part II

Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and;

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Neural Information Processing Part III

Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and;

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Neural Information Processing Part IV

Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and;

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Introduction to Statistical Machine Learning

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and;

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Machine Intelligence and Signal Processing

in the fields of machine learning, big data analytics, signal processing, computational learning theory, and their real-time applications. The;

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Bayesian Speech & Language Processing

and latent topic models, along with applications including automatic speech recognition, speaker verification, and information retrieval;

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