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图书名称： Machine Learning for Adaptive Many-Core Machines - A Practical Approach (Studies in Big Data) Hardcover – July 16, 2014
， Studies in Big Data Volume 7
作者：Noel Lopes (Author), Bernardete Ribeiro (Author)
出版社：Springer International Publishing
页数：314
出版时间：2014
语言：English
格式：pdf
内容简介：
Todaythe increasingcomplexity,performancerequirementsandcost ofcurrent(and
future) applications in society is transversal to a wide range of activities, from
science to business and industry. In particular, this is a fundamental issue in the
Machine Learning (ML) area, which is becoming increasingly relevant in a wide
diversity of domains. The scale of the data from Web growth and advances in
sensor data collection technology have been rapidly increasing the magnitude and
complexity of tasks that ML algorithms have to solve.
Much of the data that we are generating and capturing will be available
“indefinitely” since it is considered a strategic asset from which useful and
valuable information can be extracted. In this context, Machine Learning (ML)
algorithms play a vital role in providing new insights from the abundant streams
and increasingly large repositories of data. However, it is well-known that the
computational complexity of ML methodologies, often directly related with the
amount of data, is a limiting factor that can render the application of many
algorithms to real-world problems impractical. Thus, the challenge consists of
processing such large quantities of data in a realistic (useful) time frame, which
drives the need to extend the applicability of existing ML algorithms and to devise
parallel algorithms that scale well with the volume of data or, in other words, can
handle “Big Data”.
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