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Small, Medium, and Big Data: Application of Machine Learning Methods to the...

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Intel SC9, Mission College Boulevard, Santa Clara, CA

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Abstract

To provide a context for the discussion to follow, I will first briefly discuss the general characteristics of machine learning. Then, I will describe a series of problems that illustrate the successful application of machine learning methods to the solution of problems in the printing and imaging space. These problems range from the development of detailed microscale models for printer behavior; to algorithms for print and image quality assessment; to algorithms for predicting aesthetic quality of fashion photographs; to algorithms for detection and recognition of people in home and office settings. The algorithms take a variety of different forms ranging from linear regression, context-dependent linear regression, and context-dependent linear regression augmented by stochastic sample function generation; to maximum likelihood estimation; to support vector machines; to convolutional neural networks. The size of the data sets used to train these algorithms range from tens of images to tens of thousands of images.


Speaker Bio

Jan P. Allebach is Hewlett-Packard Distinguished Professor of Electrical and Computer Engineering at Purdue University, West Lafayette, Indiana. Imaging has been a central theme of his research. Algorithms developed in his laboratory have been licensed to major vendors of imaging products, and can be found in the drivers, firmware, and hardware (ASICs) of 100s of millions of units that have been sold world-wide. Allebach is a Fellow of IEEE, IS&T (The Society for Imaging Science and Technology), the National Academy of Inventors (NAI), and SPIE. He was elected to membership in the National Academy of Engineering. He received Honorary Membership from IS&T, which is its highest award. He received the Daniel E. Noble IEEE Field Award for Emerging Technologies, and the Edwin H. Land Medal from the Optical Society of America and IS&T.

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Intel SC9, Mission College Boulevard, Santa Clara, CA

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