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: The novel EOG-based HCI system allows people to successfully and economically communicate with their environment by using only eye movements and classifying horizontal and vertical EOG channel signals in an efficient interface is realized.
Abstract: The aim of this paper is to present the design and application of an electrooculogram (EOG) based on an efficient human-computer interface (HCI). Establishing an alternative channel without speaking and hand movements is important in increasing the quality of life for the handicapped. EOG-based systems are more efficient than electroencephalogram (EEG)-based systems in some cases. By using a realized virtual keyboard, it is possible to notify in writing the needs of the patient in a relatively short time. Considering the biopotential measurement pitfalls, the novel EOG-based HCI system allows people to successfully communicate with their environment by using only eye movements. Classifying horizontal and vertical EOG channel signals in an efficient interface is realized in this study. The new system is microcontroller based, with a common-mode rejection ratio of 88 dB, an electronic noise of 0.6 μV (p-p), and a sampling rate of 176 Hz. The nearest neighborhood algorithm is used to classify the signals, and the classification performance is 95%. The novel EOG-based HCI system allows people to successfully and economically communicate with their environment by using only eye movements.
169 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...The kNN technique is often applied to classify biosignals [35]....
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Posted Content•
TL;DR: This tutorial-style paper provides a high-level introduction to the basics of supervised and unsupervised learning, exemplifying applications to communication networks by distinguishing tasks carried out at the edge and at the cloud segments of the network at different layers of the protocol stack.
Abstract: Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems for which the development of conventional engineering solutions is challenged by modelling or algorithmic deficiencies. This tutorial-style paper starts by addressing the questions of why and when such techniques can be useful. It then provides a high-level introduction to the basics of supervised and unsupervised learning. For both supervised and unsupervised learning, exemplifying applications to communication networks are discussed by distinguishing tasks carried out at the edge and at the cloud segments of the network at different layers of the protocol stack.
169 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...We refer to [57] and [58] for detailed examples....
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...Analysis (PCA), dictionary learning, and neural network-based autoencoders [19], [57], [58]....
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Proceedings Article•
21 Jun 2014TL;DR: This work introduces an efficient procedure to simultaneously train a NADE model for each possible ordering of the variables, by sharing parameters across all these models.
Abstract: The Neural Autoregressive Distribution Estimator (NADE) and its real-valued version RNADE are competitive density models of multidimensional data across a variety of domains. These models use a fixed, arbitrary ordering of the data dimensions. One can easily condition on variables at the beginning of the ordering, and marginalize out variables at the end of the ordering, however other inference tasks require approximate inference. In this work we introduce an efficient procedure to simultaneously train a NADE model for each possible ordering of the variables, by sharing parameters across all these models. We can thus use the most convenient model for each inference task at hand, and ensembles of such models with different orderings are immediately available. Moreover, unlike the original NADE, our training procedure scales to deep models. Empirically, ensembles of Deep NADE models obtain state of the art density estimation performance.
168 citations
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...Bishop, 2006)....
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TL;DR: Experimental results on two datasets show that the proposed algorithm can correctly identify the discriminative frequency bands, demonstrating the algorithm's superiority over contemporary approaches in classification performance.
Abstract: In most current motor-imagery-based brain-computer interfaces (BCIs), machine learning is carried out in two consecutive stages: feature extraction and feature classification. Feature extraction has focused on automatic learning of spatial filters, with little or no attention being paid to optimization of parameters for temporal filters that still require time-consuming, ad hoc manual tuning. In this paper, we present a new algorithm termed iterative spatio-spectral patterns learning (ISSPL) that employs statistical learning theory to perform automatic learning of spatio-spectral filters. In ISSPL, spectral filters and the classifier are simultaneously parameterized for optimization to achieve good generalization performance. A detailed derivation and theoretical analysis of ISSPL are given. Experimental results on two datasets show that the proposed algorithm can correctly identify the discriminative frequency bands, demonstrating the algorithm's superiority over contemporary approaches in classification performance.
168 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...Well-known linear learning machines include Fisher discriminant analysis (FDA), perceptron of Rosenblatt, logistic regression, and support vector machines (SVMs) [24]....
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TL;DR: A framework for signal analysis of electroencephalography (EEG) that unifies tasks such as feature extraction, feature selection, feature combination, and classification, which are often independently tackled conventionally, under a regularized empirical risk minimization problem is proposed.
168 citations