1,156 resources related to Statistical learning
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.
The world's premier EDA and semiconductor design conference and exhibition. DAC features over 60 sessions on design methodologies and EDA tool developments, keynotes, panels, plus the NEW User Track presentations. A diverse worldwide community representing more than 1,000 organizations attends each year, from system designers and architects, logic and circuit designers, validation engineers, CAD managers, senior managers and executives to researchers and academicians from leading universities.
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)
The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.
The International Conference on Information Fusion is the premier forum for interchange of the latest research in data and information fusion, and its impacts on our society. The conference brings together researchers and practitioners from academia and industry to report on the latest scientific and technical advances.
The Frontiers in Education (FIE) Conference is a major international conference focusing on educational innovations and research in engineering and computing education. FIE 2019 continues a long tradition of disseminating results in engineering and computing education. It is an ideal forum for sharing ideas, learning about developments and interacting with colleagues inthese fields.
Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...
The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications.
2005 International Conference on Neural Networks and Brain, 2005
Support vector machines (SVM) is a novel machine learning method based on small-sample statistical learning theory (SLT), and is powerful for the problem with small sample, nonlinearity, high dimension, and local minima. SVM have been very successful in pattern recognition, fault diagnoses and function estimation problems. Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead ...
2009 IEEE Symposium on Industrial Electronics & Applications, 2009
Support vector machine (SVM), which is based on statistical learning theory, is a universal machine learning method. This paper proposes the application of SVM in classifying the causes of voltage sag in power distribution system. Voltage sag is among the major power quality disturbances that can cause substantial loss of product and also can attribute to malfunctions, instabilities and shorter ...
Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference, 1994
A fuzzy decision system (FDS) is proposed for condition monitoring of machining processes. The membership functions are established through a learning process based on test data, rather than being selected as a priori. The optimal partition and information gain weighting functions are also introduced in order to improve the robustness and reliability of this method. Experiment verification with an optimistic ...
6th Seminar on Neural Network Applications in Electrical Engineering, 2002
Summary form only given. This tutorial is devoted to an important segment of statistical learning techniques related to the problem of supervised learning, which aims at predicting the value of an outcome given a number of inputs. Theoretical material is oriented mainly towards methods and concepts. The introduction outlines general aspects of statistical learning, together with motivations for its applications ...
2008 19th International Conference on Pattern Recognition, 2008
Retrieving videos using key words requires obtaining the semantic features of the videos. Most work reported in the literature focuses on annotating a video shot with a fixed number of key words, no matter how much information is contained in the video shot. In this paper, we propose a new approach to automatically annotate a video shot with an adaptive ...
Vladimir Vapnik accepts the IEEE John Von Neumann Medal - Honors Ceremony 2017
Vladimir Cherkassky - Predictive Learning, Knowledge Discovery and Philosophy of Science
Sparse Fuzzy Modeling - Nikhil R Pal - WCCI 2016
Uncovering the Neural Code of Learning Control - Jennie Si - WCCI 2012 invited lecture
Social Robotics for Neurodevelopmental Disorders - ICRA 2020
Enter Deep Learning
Louis Scharf - Honors Ceremony 2016 Red Carpet Interview
Machine Learning of Motor Skills for Robotics
Linear Regression: Intro to Machine Learning Workshop - IEEE Region 4 Presentation
Federated Learning for Networking - Anwar Walid - IEEE Sarnoff Symposium, 2019
The eXtensible Event Stream (XES) standard
26th Annual MTT-AP Symposium and Mini Show - Dr. Ajay Poddar
Overcoming the Static Learning Bottleneck - the Need for Adaptive Neural Learning - Craig Vineyard: 2016 International Conference on Rebooting Computing
Perception-Action-Learning and Associative Skill Memories
IEEE Day Future Milestone: Machine Learning in the future
ICASSP 2011 Trends in Machine Learning for Signal Processing
Ensemble Approaches in Learning
Continuously Learning Neuromorphic Systems with High Biological Realism: IEEE Rebooting Computing 2017
Signal Processing and Machine Learning
Support vector machines (SVM) is a novel machine learning method based on small-sample statistical learning theory (SLT), and is powerful for the problem with small sample, nonlinearity, high dimension, and local minima. SVM have been very successful in pattern recognition, fault diagnoses and function estimation problems. Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. This paper discusses least squares support vector machines (LS-SVM) estimation algorithm and introduces applications of the novel method for the nonlinear control systems. Then identification of MIMO models and soft-sensor modeling based on least squares support vector machines (LS-SVM) is proposed. The simulation results show that the proposed method provides a powerful tool for identification and soft-sensor modeling and has promising application in industrial process applications
Support vector machine (SVM), which is based on statistical learning theory, is a universal machine learning method. This paper proposes the application of SVM in classifying the causes of voltage sag in power distribution system. Voltage sag is among the major power quality disturbances that can cause substantial loss of product and also can attribute to malfunctions, instabilities and shorter lifetime of the load. Voltage sag can be caused by fault in power system, starting of induction motor and transformer energizing. An IEEE 30 bus system is modeled using the PSCAD software to generate the data for different type of voltage sag namely, caused by fault and starting of induction motor. Feature extraction using the wavelet transformation for the SVM input has been performed prior to the classification of the voltage sag cause. Two kernels functions are used namely radial basis function (RBF) and polynomial function. The minimum and maximum of the wavelet energy are used as the input to the SVM and analysis on the performance of these two kernels are presented. In this paper, it has been found that the polynomial kernel performed better as compared to the RBF in classifying the cause of voltage sag in power system.
A fuzzy decision system (FDS) is proposed for condition monitoring of machining processes. The membership functions are established through a learning process based on test data, rather than being selected as a priori. The optimal partition and information gain weighting functions are also introduced in order to improve the robustness and reliability of this method. Experiment verification with an optimistic success rate of 97.5% was achieved.<<ETX>>
Summary form only given. This tutorial is devoted to an important segment of statistical learning techniques related to the problem of supervised learning, which aims at predicting the value of an outcome given a number of inputs. Theoretical material is oriented mainly towards methods and concepts. The introduction outlines general aspects of statistical learning, together with motivations for its applications in medicine and genomics. The second part deals with the main theoretical aspects of supervised learning, including a short overview of statistical decision theory, with the emphasis on the problem of trade-off between bias and variance. Attention is further paid to linear methods, applied to both regression and classification problems. In the presentation of neural networks applied to statistical learning, stress is placed on multi-layer perceptrons and training algorithms based on gradient search techniques. Various issues important in practice are given considerable attention, including cross-validation techniques and the choice of suitable learning procedures.
Retrieving videos using key words requires obtaining the semantic features of the videos. Most work reported in the literature focuses on annotating a video shot with a fixed number of key words, no matter how much information is contained in the video shot. In this paper, we propose a new approach to automatically annotate a video shot with an adaptive number of annotation key words according to the richness of the video content. A semantic candidate set (SCS) with fixed size is discovered using visual features. Then the final annotation set, which has an unfixed number of key words, is obtained from the SCS by using Bayesian inference, which combines static and dynamic inference to remove the irrelevant candidate key words. We have applied our approach to video retrieval. The experiments demonstrate that video retrieval using our annotation approach outperforms retrieval using a fixed number of annotation words.
Currently, most text classifiers apply machine learning methods, while ignore traditional methods based on classification rules. In this paper, we propose a strong covering algorithm (called SCA) for generating strong classification rules and view the rules-based classifier as a component classifier in the ensemble text classifier. SCA extracts noun phrase to index document based-on our proposed Exhaustive Noun-Phrase Extraction Algorithm. Experimental results show that the ensemble classifier integrating the strong rules achieves an approximately 8% improvement as compared to bi-gram classifier and 15% improvement as compared to the single rule-based classifier.
In this paper, we propose a new methodology to combine spectral information and spatial features for Support Vector Machine (SVM)-based classification. The novelty of the proposed work is in the combination of band selection (i.e., linear prediction (LP)-based method), spatial feature extraction (i.e., morphology profiles (MP)), and spectral transformation (i.e., principal component analysis (PCA)) to build a computationally tractable system. The preliminary result with ROSIS data shows that using the selected bands and MP features extracted from principal components (PCs) can yield the highest accuracy. We believe such finding is instructive to feature extraction/selection for spectral/spatial-based hyperspectral image classification.
In this work, the performance of the Knn-algorithm for classifying different kinds of signals will be analyzed. In particular, the classification will be between two groups: voice and music signals. The distinctive features of speech signals will be exploited to separate them from musical ones. The classification will be based on mean and deviation of the amount of peaks from each spectogram line. In order to adapt the concept of line, thresholds have to be set. Finally, some improvements will be proposed, based on the obtention of other features and the setting of new thresholds for enhancement of performance.
Altering the shape of the plunger and the core changes the working air gap of cylinder valve in the hydrogen cell car. The electromagnetic force can change greatly depending on the design of the internal end of the plunger and the core. The Plunger and core parameters are selected based on large amounts of finite element experiments in the design. Sometimes this work cannot be completed actually. This paper presents a model which input is the plunger and core parameters and output is the force based on support vector machine. Simulation analysis indicates that this model has high learning speed, good approximation, well generalization ability.
This paper reconstructs multivariate functions from scattered data by a new multiscale technique. The reconstruction uses support vector regression model by positive definite reproducing kernels in Hilbert spaces. But it adopts techniques from wavelet theory and shift-invariant spaces to construct a new class of kernels as multiscale superpositions of shifts and scales of a single compactly supported function. This means that the advantages of scaled regular grids are used to construct the kernels, while the advantages of unrestricted scattered data interpolation are maintained after the kernels are constructed. Using such a multiscale kernel, the reconstruction method interpolates at given scattered data. No manipulations of the data are needed. Then, the multiscale structure of the kernel allows to represent the interpolant on regular grids on all scales involved, with cheap evaluation due to the compact support of the function, and with a recursive evaluation technique in support vector regression. Numerical comparisons are given in two dimensions which show competitive results with the single-variable two-variable function, the new model not only can reconstruct linear and the non-linear combination functions very well, but also performs better in multivariate functions reconstruction. The results indicate that the proposed method has effectiveness in terms of both objective measurements and visual evaluation.
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Applied Statistics Postdoctoral Research Staff Member
Lawrence Livermore National Laboratory
Lawrence Livermore National Laboratory
Data Scientist - Early Career
Lawrence Livermore National Laboratory