الصفحة 1
الصفحة 1
Modern deep learning for tabular data : Novel approaches to common modeling problems
Synthesizes and presents novel deep learning approaches to a seemingly unlikely domain - tabular data. Whether for finance, business, security, medicine, or countless other domain, deep learning can help mine and model complex patterns in tabular data - an incredibly ubiquitous form of structured data. Part I of the book offers a rigorous overview of machine learning principles, algorithms, and implementation skills relevant to holistically modeling and manipulating tabular data. Part II studies five dominant deep learning model designs - Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Attention and Transformers, and Tree-Rooted Networks - through both their 'default' usage and their application to tabular data. Part III compounds the power of the previously covered methods by surveying strategies and techniques to supercharge deep learning systems: autoencoders, deep data generation, meta-optimization, multi-model arrangement, and neural network interpretability.
Formation and Early Growth of Business Webs: Modular Product Systems in Network Markets
Networks of firms have been in the focus of management research for several years. Recently, special attention has been paid to so-called business webs. Business webs are networks of firms which provide complements to a common product architecture.The present book explicitly examines the formation and early growth of business webs. The author illustrates the early growth phases with two in-depth cases of the formation of the wireless internet ecosystem i-mode and the leading person-to-person online auction platform eBay. The book uncovers the contingencies under which the establishment of business webs is likely to succeed. Business researchers will benefit from the theoretical framework, while interested business managers will find explanations and advice for establishing a business web.
CMOS single chip fast frequency hopping synthesizers for Wireless multi-gigahertz applications : Design methodology, analysis, and implementation
Describes an efficient design and characterization methodology that has been developed to study loop trade-offs in both open and close loop modelling techniques. This is based on a simulation platform that incorporates both behavioral models and measured/simulated sub-blocks of the chosen frequency synthesizer. The platform predicts accurately the phase noise, spurious and switching performance of the final design. Therefore excellent phase noise and spurious performance can be achieved while meeting all the specified requirements. The design methodology reduces the need for silicon re-spin enabling circuit designers to directly meet cost, performance and schedule milestones. The developed knowledge and techniques have been used in the successful design and implementation of two high speed multi-mode fractional-N frequency synthesizers for the IEEE 801.11a/b/g standards. Both synthesizer designs are described in details.
Journal on Data Semantics XI
The LNCS Journal on Data Semantics is devoted to the presentation of notable work that, in one way or another, addresses research and development on issues related to data semantics. The scope of the journal ranges from theories supporting the formal definition of semantic content to innovative domain-specific applications of semantic knowledge. The journal addresses researchers and advanced practitioners working on the semantic web, interoperability, mobile information services, data warehousing, knowledge representation and reasoning, conceptual database modeling, ontologies, and artificial intelligence.
