This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and machine learning. Broad learning aims at fusing multiple large-scale information sources of diverse varieties together, and carrying out synergistic data mining tasks across these fused sources in one unified analytic. This book takes online social networks as an application example to introduce the latest alignment and knowledge discovery algorithms. Besides the overview of broad learning, machine learning and social network basics, specific topics covered in this book include network alignment, link prediction, community detection, information diffusion, viral marketing, and network embedding.

Encapsulation Technologies for Electronic Applications, Second Edition, offers an updated, comprehensive discussion of encapsulants in electronic applications, with a primary emphasis on the encapsulation of microelectronic devices and connectors and transformers. It includes sections on 2-D and 3-D packaging and encapsulation, encapsulation materials, including environmentally friendly 'green' encapsulants, and the properties and characterization of encapsulants. Furthermore, this book provides an extensive discussion on the defects and failures related to encapsulation, how to analyze such defects and failures, and how to apply quality assurance and qualification processes for encapsulated packages. In addition, users will find information on the trends and challenges of encapsulation and microelectronic packages, including the application of nanotechnology. Increasing functionality of semiconductor devices and higher end used expectations in the last 5 to 10 years has driven development in packaging and interconnected technologies. The demands for higher miniaturization, higher integration of functions, higher clock rates and data, and higher reliability influence almost all materials used for advanced electronics packaging, hence this book provides a timely release on the topic.

This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and machine learning. Broad learning aims at fusing multiple large-scale information sources of diverse varieties together, and carrying out synergistic data mining tasks across these fused sources in one unified analytic. This book takes online social networks as an application example to introduce the latest alignment and knowledge discovery algorithms. Besides the overview of broad learning, machine learning and social network basics, specific topics covered in this book include network alignment, link prediction, community detection, information diffusion, viral marketing, and network embedding.

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