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Journal ArticleDOI

Pattern Recognition and Machine Learning

01 Aug 2007-Technometrics (Taylor & Francis)-Vol. 49, Iss: 3, pp 366-366
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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Journal ArticleDOI
TL;DR: A comprehensive review of DL as well as its implications upon the healthcare is presented in this review, which had analysed 150 articles of DL in healthcare domain from PubMed, Google Scholar, and IEEE EXPLORE focused in medical imagery only.

225 citations


Cites background from "Pattern Recognition and Machine Lea..."

  • ...This discovery led to the foundation of artificial neural networks (ANNs) [2] and subsequently deep learning [3]....

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Journal ArticleDOI
TL;DR: Applying the BrainAGE framework to preterm-born adolescents resulted in a significantly lower estimated brain age than chronological age in subjects who were born before the end of the 27th week of gestation, demonstrating the successful clinical application and future potential of this method.

224 citations


Cites background from "Pattern Recognition and Machine Lea..."

  • ...More details can be found elsewhere (Bishop, 2006; Schölkopf and Smola, 2002; Tipping, 2000)....

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  • ...Additionally, inter-regional dependencies are taken into account (Bishop, 2006; Schölkopf and Smola, 2002), such as the widespread microstructural changes inWM, which were recently found to be associated with corresponding age-related changes in cortical GM regions in adolescents (Giorgio et al....

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  • ...Additionally, inter-regional dependencies are taken into account (Bishop, 2006; Schölkopf and Smola, 2002), such as the widespread microstructural changes inWM, which were recently found to be associated with corresponding age-related changes in cortical GM regions in adolescents (Giorgio et al.,…...

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Journal ArticleDOI
TL;DR: The results suggest that the analysis of variable importance with respect to the different classifiers and travel modes is essential for a better understanding and effective modeling of peoples travel behavior.
Abstract: A comparison of 7 classifiers for travel mode prediction is performed.Prediction accuracy and variable importance for each travel mode is investigated.Among the investigated classifiers, random forest performs best.Trip distance followed by the number of cars are the most important variables.The importance of other variables varies with travel mode and classifier. The analysis of travel mode choice is an important task in transportation planning and policy making in order to understand and predict travel demands. While advances in machine learning have led to numerous powerful classifiers, their usefulness for modeling travel mode choice remains largely unexplored. Using extensive Dutch travel diary data from the years 2010 to 2012, enriched with variables on the built and natural environment as well as on weather conditions, this study compares the predictive performance of seven selected machine learning classifiers for travel mode choice analysis and makes recommendations for model selection. In addition, it addresses the importance of different variables and how they relate to different travel modes. The results show that random forest performs significantly better than any other of the investigated classifiers, including the commonly used multinomial logit model. While trip distance is found to be the most important variable, the importance of the other variables varies with classifiers and travel modes. The importance of the meteorological variables is highest for support vector machine, while temperature is particularly important for predicting bicycle and public transport trips. The results suggest that the analysis of variable importance with respect to the different classifiers and travel modes is essential for a better understanding and effective modeling of peoples travel behavior.

224 citations


Cites background from "Pattern Recognition and Machine Lea..."

  • ...In addition, it can lead to inconsistent results in which 298 observations are assigned to multiple classes simultaneously (Bishop, 2006)....

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  • ...Instead of making strict assumptions about the data, machine learning models50 learn to represent complex relationships in a data-driven manner (e.g. Bishop, 2006)....

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  • ...In addition, it can lead to inconsistent results in which298 observations are assigned to multiple classes simultaneously (Bishop, 2006)....

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Journal ArticleDOI
TL;DR: An EMT algorithm with explicit genetic transfer across tasks, namely EMT via autoencoding, which allows the incorporation of multiple search mechanisms with different biases in the EMT paradigm is proposed.
Abstract: Evolutionary multitasking (EMT) is an emerging research topic in the field of evolutionary computation. In contrast to the traditional single-task evolutionary search, EMT conducts evolutionary search on multiple tasks simultaneously. It aims to improve convergence characteristics across multiple optimization problems at once by seamlessly transferring knowledge among them. Due to the efficacy of EMT, it has attracted lots of research attentions and several EMT algorithms have been proposed in the literature. However, existing EMT algorithms are usually based on a common mode of knowledge transfer in the form of implicit genetic transfer through chromosomal crossover. This mode cannot make use of multiple biases embedded in different evolutionary search operators, which could give better search performance when properly harnessed. Keeping this in mind, this paper proposes an EMT algorithm with explicit genetic transfer across tasks, namely EMT via autoencoding, which allows the incorporation of multiple search mechanisms with different biases in the EMT paradigm. To confirm the efficacy of the proposed EMT algorithm with explicit autoencoding, comprehensive empirical studies have been conducted on both the single- and multi-objective multitask optimization problems.

224 citations


Additional excerpts

  • ...solution for ordinary least squares [38], which is given by...

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Proceedings ArticleDOI
23 Jun 2014
TL;DR: It is shown that better action recognition using skeletal features can be achieved by replacing gaussian mixture models by deep neural networks that contain many layers of features to predict probability distributions over states of hidden Markov models.
Abstract: Over the last few years, with the immense popularity of the Kinect, there has been renewed interest in developing methods for human gesture and action recognition from 3D skeletal data. A number of approaches have been proposed to extract representative features from 3D skeletal data, most commonly hard wired geometric or bio-inspired shape context features. We propose a hierarchial dynamic framework that first extracts high level skeletal joints features and then uses the learned representation for estimating emission probability to infer action sequences. Currently gaussian mixture models are the dominant technique for modeling the emission distribution of hidden Markov models. We show that better action recognition using skeletal features can be achieved by replacing gaussian mixture models by deep neural networks that contain many layers of features to predict probability distributions over states of hidden Markov models. The framework can be easily extended to include a ergodic state to segment and recognize actions simultaneously.

224 citations


Cites methods from "Pattern Recognition and Machine Lea..."

  • ...…observation states p(Xt|Ht); to verify that the temporal incorporation in our model is a more effective approach for action recognition against the Bag-of-Visual-Word approach, we compare against the EigenJoint-Naive Bayes Nearest Neighbour [30] where the same set of raw features have been used....

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  • ...Currently the model parameters are predominantly learnt by Gaussian mixture models using expectation maximization [1, 21]....

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