Optimization in Machine Learning and Applications / / edited by Anand J. Kulkarni, Suresh Chandra Satapathy

• Pubblicazione: Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020
• Edizione: 1st ed. 2020.
• Descrizione fisica: 1 online resource (202 pages)
• Disciplina: 006.31
• Soggetto topico: Computational intelligence; Machine learning; Mathematical optimization; Algorithms; Computational Intelligence; Machine Learning; Optimization; Algorithm Analysis and Problem Complexity
• Persona (resp. second.): KulkarniAnand J; SatapathySuresh Chandra
• Nota di contenuto: Use of Artificial Neural Network for Abnormality Detection in Medical Images -- Deep Learning Techniques for Crime Hotspot Detection -- Optimization Techniques for Machine Learning -- A Package Including Pre-Processing, Feature Extraction, Feature Reduction, and Classification for MRI Classification -- Predictive Analysis of Lake Water Quality Using an Evolutionary Algorithm -- A Survey on the Latest Development of Machine Learning in Genetic Algorithm and Particle Swam Optimization -- A Hybridized Data Clustering for Breast Cancer Prognosis and Risk Exposure using Fuzzy C-Means and Cohort Intelligence -- Development of Algorithm for Spatial Modelling of Climate Data for Agriculture Management for the Semi-Arid Area of Maharashtra in India -- A Survey on Human Group Activity Recognition by Analyzing Person Action from Video Sequences using Machine Learning Techniques -- Artificial Intelligence in Journalism: A Boon or Bane? -- A Perspective of Artificial Intelligence in Public Relations: The Way Forward -- Roulette Wheel Selection Based Computational Intelligence Technique to Design an Efficient Transmission Policy for Energy Harvesting Sensors.
• Sommario/riassunto: This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.
• Titolo autorizzato: Optimization in Machine Learning and Applications
• ISBN: 981-15-0994-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910484513003321
• Lo trovi qui: Univ. Federico II
• Serie: Algorithms for Intelligent Systems, . 2524-7565