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 work proposes to complement head-to-head scaling studies that compare quantum annealing machines to state-of-the-art classical codes with an approach that compares the performance of different algorithms and/or computing architectures on different classes of computationally hard tunable spin-glass instances.
Abstract: While manufacturing limitations are imposing constraints on Moore's law, researchers are searching for novel computing architectures based on quantum-mechanical effects. However, it remains to be shown that quantum annealing techniques consistently outperform classical simulated annealing to minimize optimization problems.
127 citations
TL;DR: The experimental results show that by combining the measurements from both sensor systems, this paper could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST).
Abstract: Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbour. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real time stress detection. Finally, we present an study of the most discriminative features for stress detection.
127 citations
TL;DR: This paper proposes a new variational approximation for infinite mixtures of Gaussian processes that uses variational inference and a truncated stick-breaking representation of the Dirichlet process to approximate the posterior of hidden variables involved in the model.
Abstract: This paper proposes a new variational approximation for infinite mixtures of Gaussian processes. As an extension of the single Gaussian process regression model, mixtures of Gaussian processes can characterize varying covariances or multimodal data and reduce the deficiency of the computationally cubic complexity of the single Gaussian process model. The infinite mixture of Gaussian processes further integrates a Dirichlet process prior to allowing the number of mixture components to automatically be determined from data. We use variational inference and a truncated stick-breaking representation of the Dirichlet process to approximate the posterior of hidden variables involved in the model. To fix the hyperparameters of the model, the variational EM algorithm and a greedy algorithm are employed. In addition to presenting the variational infinite-mixture model, we apply it to the problem of traffic flow prediction. Experiments with comparisons to other approaches show the effectiveness of the proposed model.
127 citations
Cites result from "Pattern Recognition and Machine Lea..."
...We will also compare this new approach with some other traffic prediction methods, including one stateof-the-art method BNs....
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...Because p(y|x, z = k, Ω̂,Θ) is Gaussian distributed, the prediction ŷ for the new input x would be the weighted average of the T Gaussian means, and the weights are given by p(z = k|Ω̂)p(x|z = k, Ω̂)/∑Ti=1 p(z = i|Ω̂)p(x|z = i, Ω̂) (k = 1, . . . , T )....
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TL;DR: This study demonstrates the high potential of current machine learning techniques to predict system-specific clinical outcome even for a disease as heterogeneous as stroke.
Abstract: An accurate prediction of system-specific recovery after stroke is essential to provide rehabilitation therapy based on the individual needs. We explored the usefulness of functional magnetic resonance imaging scans from an auditory language comprehension experiment to predict individual language recovery in 21 aphasic stroke patients. Subjects with an at least moderate language impairment received extensive language testing 2 weeks and 6 months after left-hemispheric stroke. A multivariate machine learning technique was used to predict language outcome 6 months after stroke. In addition, we aimed to predict the degree of language improvement over 6 months. 76% of patients were correctly separated into those with good and bad language performance 6 months after stroke when based on functional magnetic resonance imaging data from language relevant areas. Accuracy further improved (86% correct assignments) when age and language score were entered alongside functional magnetic resonance imaging data into the fully automatic classifier. A similar accuracy was reached when predicting the degree of language improvement based on imaging, age and language performance. No prediction better than chance level was achieved when exploring the usefulness of diffusion weighted imaging as well as functional magnetic resonance imaging acquired two days after stroke. This study demonstrates the high potential of current machine learning techniques to predict system-specific clinical outcome even for a disease as heterogeneous as stroke. Best prediction of language recovery is achieved when the brain activation potential after system-specific stimulation is assessed in the second week post stroke. More intensive early rehabilitation could be provided for those with a predicted poor recovery and the extension to other systems, for example, motor and attention seems feasible.
127 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...For technical information on SVMs, we refer to the textbooks by Vapnik (1998) and Bishop (2006)....
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TL;DR: A new heuristic to combine any type of one-class models for solving the multi-class classification problem with outlier rejection is proposed, which normalizes the average model output per class, instead of the more common non-linear transformation of the distances.
127 citations
Cites background from "Pattern Recognition and Machine Lea..."
...To distinguish this outlier class from the c known classes, one can put a threshold on the total data density of the known classes (Bishop, 2006)....
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