Meta-analytics : Consensus Approaches and System Patterns for Data Analysis

Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of traditional analytics approaches. The book is ‘meta’ to analytics, covering general analytics in sufficient detail for readers to engage with, and understand, hybrid or meta- approaches. The book has relevance to machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance.

Inn addition, the analytics within can be applied to predictive algorithms for everyone from police departments to sports analysts.

• Provides comprehensive and systematic coverage of machine learning-based data analysis tasks
• Enables rapid progress towards competency in data analysis techniques
• Gives exhaustive and widely applicable patterns for use by data scientists
• Covers hybrid or ‘meta’ approaches, along with general analytics
• Lays out information and practical guidance on data analysis for practitioners working across all sectors

Steven J Simske is HP Fellow and Director at Hewlett Packard Labs, and has worked in machine intelligence and analytics for the past 25 years, with domains extending from medical image analytics to text summarization. He has performed research relevant to meta analytics for over 20 years at HP Labs, and in collaboration with major universities in the US and Brazil.