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  Browse All Reviews > Computing Methodologies (I) > Artificial Intelligence (I.2) > Learning (I.2.6)  
  1-10 of 1067 Reviews about "Learning (I.2.6)": Date Reviewed
  Answer set programming for non-stationary Markov decision processes
Ferreira L., Bianchi R., Santos P., Lopez de Mantaras R.  Applied Intelligence 47(4): 993-1007, 2017. Type: Article

Problem solving with computers often involves the exploration of paths from an initial state to a goal state. In addition to the size of this search space, there are many factors complicating this approach, especially in realistic environments. In...

Mar 13 2018
  Learning Bayesian network parameters from small data sets
Guo Z., Gao X., Ren H., Yang Y., Di R., Chen D.  International Journal of Approximate Reasoning 91 22-35, 2017. Type: Article

Bayesian networks (BNs) represent a powerful statistical tool for uncertainty analysis with applications in many areas, for example, medical diagnosis. Since data is often not sufficiently available to accurately learn the parameters of a BN by th...

Mar 12 2018
  An introduction to machine learning (2nd ed.)
Kubat M.,  Springer International Publishing, New York, NY, 2017. 348 pp. Type: Book (978-3-319639-12-3)

In his introduction, the author states that “machine learning has come of age.” In many ways, if the scope and methods are those established in the book, it is true. But from the point of view of recent research and especially industri...

Mar 9 2018
  Optimizing search results for human learning goals
Syed R., Collins-Thompson K.  Information Retrieval 20(5): 506-523, 2017. Type: Article

For many students, web search is an important part of the learning process. However, existing search engines are optimized so as to achieve the largest average customer satisfaction among different categories of customers. Because of this, search ...

Feb 7 2018
  Deep learning for computer architects
Reagen B., Adolf R., Whatmough P., Wei G., Brooks D.,  Morgan & Claypool Publishers, San Rafael, CA, 2017. 124 pp. Type: Book (978-1-627057-28-8)

I was looking for a book on deep learning that could put the emphasis on efficiency, rather than achieving task accuracy at whatever cost. Although this book was not meant for this purpose, it changed my perception completely. It is an invaluable ...

Jan 31 2018
  Predictive data mining models
Olson D., Wu D.,  Springer International Publishing, New York, NY, 2017. 102 pp. Type: Book

In an age of artificial intelligence connected primarily to large amounts of constantly changing data, this book’s topic is extremely interesting. It focuses on a demonstration of predictive methods and tools using real business-related data...

Jan 31 2018
  Encyclopedia of machine learning and data mining (2nd ed.)
Sammut C., Webb G.,  Springer International Publishing, New York, NY, 2017. 1335 pp. Type: Book (978-1-489976-85-7)

The quest for developing intelligent machines has a long history. In the year 1950, Alan Turing ushered in his imitation game as a way of finding out if a machine could be regarded as being intelligent. Over the years, machine learning has integra...

Jan 29 2018
   Deep learning for mobile multimedia: a survey
Ota K., Dao M., Mezaris V., De Natale F.  ACM Transactions on Multimedia Computing, Communications, and Applications 13(3s): 1-22, 2017. Type: Article

Deep learning architectures, tools, and algorithms are, in general, not adapted to the storage and computation resources of a mobile device. Thus, one needs better hardware for mobile devices and smaller footprints for learning and inference algor...

Jan 29 2018
  Artificial intelligence: foundations of computational agents (2nd ed.)
Poole D., Mackworth A.,  Cambridge University Press, New York, NY, 2017. 760 pp. Type: Book (978-1-107195-39-4)

Artificial intelligence (AI) can be defined as the study of the design of intelligent computational agents. Unfortunately, this is a recursive definition caused by the lack of a consensus on what intelligence really means. In the past, it could re...

Jan 23 2018
  Introduction to machine learning with applications in information security
Stamp M.,  Chapman & Hall/CRC, Boca Raton, FL, 2017. 364 pp. Type: Book (978-1-138626-78-2)

Machine learning and information security are well-established disciplines that benefit mutually from their interaction because many modern network and system intrusion and prevention systems rely on advanced anomaly detection models in order to d...

Jan 17 2018
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