Journal ArticleDOI
Pattern Recognition and Machine Learning
TLDR
This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.Abstract:
(2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.read more
Citations
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Journal ArticleDOI
Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals
TL;DR: In this article, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed, and supervised machine learning algorithms, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical signal to noise ratio estimation and modulation format classification, respectively.
Journal ArticleDOI
Technological Exploration of RRAM Crossbar Array for Matrix-Vector Multiplication
Lixue Xia,Peng Gu,Boxun Li,Tianqi Tang,Xiling Yin,Wenqin Huangfu,Shimeng Yu,Yu Cao,Yu Wang,Huazhong Yang +9 more
TL;DR: This paper analyzes the impact of both device level and circuit level non-ideal factors, including the nonlinear current-voltage relationship of RRAM devices, the variation of device fabrication and write operation, and the interconnect resistance as well as other crossbar array parameters, and proposes a technological exploration flow for device parameter configuration.
Journal ArticleDOI
Tracking Pedestrians Using Local Spatio-Temporal Motion Patterns in Extremely Crowded Scenes
Louis Kratz,Ko Nishino +1 more
TL;DR: This paper represents the crowd motion with a collection of hidden Markov models trained on local spatio-temporal motion patterns, i.e., the motion patterns exhibited by pedestrians as they move through local space-time regions of the video.
Book ChapterDOI
Patch complexity, finite pixel correlations and optimal denoising
TL;DR: A law of diminishing return is presented, namely that with increasing patch size, rare patches not only require a much larger dataset, but also gain little from it, and this result suggests novel adaptive variable-sized patch schemes for denoising.
Journal ArticleDOI
Empirical Mode Decomposition Based Deep Learning for Electricity Demand Forecasting
Jatin Bedi,Durga Toshniwal +1 more
TL;DR: An empirical mode decomposition (EMD)-based deep learning approach which combines the EMD method with the long short-term memory network model to estimate electricity demand for the given season, day, and time interval of a day is proposed.