機械学習の応用 Applications of Machine Learning (Algorithms for Intelligent Systems)

Johri, Prashant (EDT)/ Verma, Jitendra Kumar (EDT)/ Paul, Sudip (EDT)

Springer Verlag, Singapore（2020/05発売）

• 製本 Hardcover:ハードカバー版／ページ数 394 p.
• 言語 ENG
• 商品コード 9789811533563
• DDC分類 620

Full Description

This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics.

Contents

Statistical Learning Process for the Reduction of Sample Collection Assuring a Desired Level of Confidence.- Sentiment Analysis on Google Play Store Data using Deep Learning.- Managing the Data Meaning in the Data Stream Processing: A Systematic Literature Mapping.- Tracking an Object using Traditional MS (Mean Shift) and CBWH MS (Mean Shift) Algorithm with Kalman Filter.- Transfer Learning and Domain Adaptation for Named Entity Recognition.- Knowledge Graph from Informal Text: Architecture, Components, Algorithms and Applications.- Neighborhood-based Collaborative Recommendations: An Introduction.- Classification of Arabic Texts Using Singular Value Decomposition and Fuzzy C-Means Algorithms.- Echo State Network Based Nonlinear Channel Equalization in Wireless Communication System.- Melody Extraction from Music: A Comprehensive Study.- Comparative Analysis of Combined Gas Turbine-Steam Turbine Power Cycle Performance by Using Entropy Generation and Statistical Methodology.- Data Mining - A Tool for Handling Huge Voluminous Data.- Improved Training Pattern in Back Propagation Neural Networks Using Holt-Winters' Seasonal Method and Gradient Boosting Model.- Ensemble of Multi-headed Machine Learning Architectures for Time-series Forecasting of Healthcare Expenditures.- Applying Soft Computing Approaches To Investigate Software Fault Proneness in Agile Software Development Environment.