44,759 research outputs found
Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel
Support vector data description (SVDD) is a machine learning technique that
is used for single-class classification and outlier detection. The idea of SVDD
is to find a set of support vectors that defines a boundary around data. When
dealing with online or large data, existing batch SVDD methods have to be rerun
in each iteration. We propose an incremental learning algorithm for SVDD that
uses the Gaussian kernel. This algorithm builds on the observation that all
support vectors on the boundary have the same distance to the center of sphere
in a higher-dimensional feature space as mapped by the Gaussian kernel
function. Each iteration involves only the existing support vectors and the new
data point. Moreover, the algorithm is based solely on matrix manipulations;
the support vectors and their corresponding Lagrange multiplier 's
are automatically selected and determined in each iteration. It can be seen
that the complexity of our algorithm in each iteration is only , where
is the number of support vectors. Experimental results on some real data
sets indicate that FISVDD demonstrates significant gains in efficiency with
almost no loss in either outlier detection accuracy or objective function
value.Comment: 18 pages, 1 table, 4 figure
Spatial support vector regression to detect silent errors in the exascale era
As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt the executionresults of HPC applications without being detected. In this work, we explore a low-memory-overhead SDC detector, by leveraging epsilon-insensitive support vector machine regression, to detect SDCs that occur in HPC applications that can be characterized by an impact error bound. The key contributions are three fold. (1) Our design takes spatialfeatures (i.e., neighbouring data values for each data point in a snapshot) into training data, such that little memory overhead (less than 1%) is introduced. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show thatour detector can achieve the detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% of false positive rate for most cases. Our detector incurs low performance overhead, 5% on average, for all benchmarks studied in the paper. Compared with other state-of-the-art techniques, our detector exhibits the best tradeoff considering the detection ability and overheads.This work was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing
Research Program, under Contract DE-AC02-06CH11357, by FI-DGR 2013 scholarship, by HiPEAC PhD Collaboration
Grant, the European Community’s Seventh Framework Programme [FP7/2007-2013] under the Mont-blanc 2 Project (www.montblanc-project.eu), grant agreement no. 610402, and TIN2015-65316-P.Peer ReviewedPostprint (author's final draft
Magnetic and radar sensing for multimodal remote health monitoring
With the increased life expectancy and rise in health conditions related to aging, there is a need for new technologies that can routinely monitor vulnerable people, identify their daily pattern of activities and any anomaly or critical events such as falls. This paper aims to evaluate magnetic and radar sensors as suitable technologies for remote health monitoring purpose, both individually and fusing their information. After experiments and collecting data from 20 volunteers, numerical features has been extracted in both time and frequency domains. In order to analyse and verify the validation of fusion method for different classifiers, a Support Vector Machine with a quadratic kernel, and an Artificial Neural Network with one and multiple hidden layers have been implemented. Furthermore, for both classifiers, feature selection has been performed to obtain salient features. Using this technique along with fusion, both classifiers can detect 10 different activities with an accuracy rate of approximately 96%. In cases where the user is unknown to the classifier, an accuracy of approximately 92% is maintained
Automated Discrimination of Pathological Regions in Tissue Images: Unsupervised Clustering vs Supervised SVM Classification
Recognizing and isolating cancerous cells from non pathological tissue areas (e.g. connective stroma) is crucial for fast and objective immunohistochemical analysis of tissue images. This operation allows the further application of fully-automated techniques for quantitative evaluation of protein activity, since it avoids the necessity of a preventive manual selection of the representative pathological areas in the image, as well as of taking pictures only in the pure-cancerous portions of the tissue. In this paper we present a fully-automated method based on unsupervised clustering that performs tissue segmentations highly comparable with those provided by a skilled operator, achieving on average an accuracy of 90%. Experimental results on a heterogeneous dataset of immunohistochemical lung cancer tissue images demonstrate that our proposed unsupervised approach overcomes the accuracy of a theoretically superior supervised method such as Support Vector Machine (SVM) by 8%
Robust Modeling of Epistemic Mental States
This work identifies and advances some research challenges in the analysis of
facial features and their temporal dynamics with epistemic mental states in
dyadic conversations. Epistemic states are: Agreement, Concentration,
Thoughtful, Certain, and Interest. In this paper, we perform a number of
statistical analyses and simulations to identify the relationship between
facial features and epistemic states. Non-linear relations are found to be more
prevalent, while temporal features derived from original facial features have
demonstrated a strong correlation with intensity changes. Then, we propose a
novel prediction framework that takes facial features and their nonlinear
relation scores as input and predict different epistemic states in videos. The
prediction of epistemic states is boosted when the classification of emotion
changing regions such as rising, falling, or steady-state are incorporated with
the temporal features. The proposed predictive models can predict the epistemic
states with significantly improved accuracy: correlation coefficient (CoERR)
for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for
Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special
Issue: Socio-Affective Technologie
- …