Pattern Recognition and Machine Learning
TL;DR: 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.
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TL;DR: This review shows how ANNs can be effectively applied for catalysis prediction, the design of new catalysts, and the understanding of catalytic structures.
Abstract: Machine learning has proven to be a powerful technique during the past decades. Artificial neural network (ANN), as one of the most popular machine learning algorithms, has been widely applied to various areas. However, their applications for catalysis were not well-studied until recent decades. In this review, we aim to summarize the applications of ANNs for catalysis research reported in the literature. We show how this powerful technique helps people address the highly complicated problems and accelerate the progress of the catalysis community. From the perspectives of both experiment and theory, this review shows how ANNs can be effectively applied for catalysis prediction, the design of new catalysts, and the understanding of catalytic structures.
178 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...Machine learning, a powerful technique of artificial intelligence (AI), has been widely used for numerical prediction [1,2], classification [3,4], and pattern recognition [5,6]....
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TL;DR: This paper proposes a sparse approximation to a robust vector field learning method, sparse vector field consensus (SparseVFC), and derives a statistical learning bound on the speed of the convergence, and applies SparseVFC to the mismatch removal problem.
178 citations
Additional excerpts
...We follow the standard notations [40] and omit some terms that are independent of θ....
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TL;DR: An overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation is described, to help radiology practices prepare for the future and realize performance improvement and efficiency gains.
Abstract: Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional "machine radiologist" is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains.
178 citations
Cites background from "Pattern Recognition and Machine Lea..."
...predictive models that detect signals, classify patterns, or prognosticate outcomes [3]....
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TL;DR: A sophisticated deep-learning technique for short-term and long-term wind speed forecast, i.e., the predictive deep Boltzmann machine (PDBM) and corresponding learning algorithm and prediction accuracy of the PDBM model outperforms existing methods by more than 10%.
Abstract: It is important to forecast the wind speed for managing operations in wind power plants. However, wind speed prediction is extremely complex and difficult due to the volatility and deviation of the wind. As existing forecasting methods directly model the raw wind speed data, it is difficult for them to provide higher inference accuracy. Differently, this paper presents a sophisticated deep-learning technique for short-term and long-term wind speed forecast, i.e., the predictive deep Boltzmann machine (PDBM) and corresponding learning algorithm. The proposed deep model forecasts wind speed by analyzing the higher level features abstracted from lower level features of the wind speed data. These automatically learnt features are very informative and appropriate for the prediction. The proposed PDBM is a deep stochastic model that can represent the wind speed very well, and is inspired by two aspects. 1)The stochastic model is suitable to capture the probabilistic characteristics of wind speed. 2)Recent developments in neural networks with deep architectures show that deep generative models have competitive capability to approximate nonlinear and nonsmooth functions. The evaluation of the proposed PDBM model is depicted by both hour-ahead and day-ahead prediction experiments based on real wind speed datasets. The prediction accuracy of the PDBM model outperforms existing methods by more than 10%.
178 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...The unbiased samples of EP [x · h] can be obtained by performing alternating Gibbs sampling [34] as illustrated in Fig....
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TL;DR: An overview and discussion of specific health conditions, behavioral factors, and biological mechanisms that mark declining mobility and physical function and promising interventions to counter these effects are provided.
178 citations