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Geodesic Discriminant Analysis for Manifold-Valued Data

Maxime Louis, Benjamin Charlier, Stanley Durrleman
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
We perform dimension reduction and classification on the kimia-216 dataset and on a set of 3D brain structures segmented from Alzheimer's disease and control subjects, recovering state-of-the-art performances  ...  The generalizations of those two methods, which are equivalent in the linear case, are in general different in the manifold case. We illustrate the first generalization on the sphere S 2 .  ...  This saves the computation of the normalization constant of the Riemannian normal distribution and of geodesic distances.  ... 
doi:10.1109/cvprw.2018.00073 dblp:conf/cvpr/LouisCD18 fatcat:qumb3z3rere6bjy7img5kyrzdi

Page 2035 of Mathematical Reviews Vol. 54, Issue 6 [page]

1977 Mathematical Reviews  
If one uses instead a unary coding of satisfiable formulas in conjunctive normal form with 3 literals per conjunct, then one can obtain an NP-complete one-way non- deterministic checking stack automaton  ...  The present author treats the problem in the non-parametric case. Estimates of the optimal discriminant function are given by the use of the dynamic stochas- tic approximation method of V.  ... 

Probabilistic -calculus and Quantitative Program Analysis

A. Di Pierro
2005 Journal of Logic and Computation  
We show how the framework of probabilistic abstract interpretation can be applied to statically analyse a probabilistic version of the λ-calculus.  ...  After introducing a linear operator based semantics for our probabilistic λ-calculus Λp, and reviewing the framework of abstract interpretation and strictness analysis, we demonstrate our technique by  ...  The probabilistic β-reduction allows us to compute β-normal forms for classical terms together with information on the probability of actually achieving them by β-reduction.  ... 
doi:10.1093/logcom/exi008 fatcat:vbiwhvtqsrbarix4ef6qd22w4e

Epileptic Seizure: Classification Using Autoregression Features

Rajendran T, Sridhar K P, Vidhupriya P, Gayathri N, Anitha T
2021 International Journal of Current Research and Review  
Methods: In this research, the Probabilistic Neural Network (PNN) is considered for classifying the brain tissue samples by mapping the input pattern to several classifications.  ...  Many types of research of classification algorithms have been published, but none has effectively focused on implementing them in brain Epileptic Seizure Electroencephalography pattern analyses and lobe  ...  Louis Korczowski, Ph.D., and his team, GIPSA-lab, University of Grenoble-Alpes, France, for their P300 BCI (bi2014a) EEG open-access dataset which is used for normal brain activity classification in this  ... 
doi:10.31782/ijcrr.2021.13429 fatcat:3muca4rgi5ccfoaakcynqfc5dy

Probabilistic Granule Analysis [chapter]

Ivo Düntsch, Günther Gediga
2008 Lecture Notes in Computer Science  
We present a semi-parametric approach to evaluate the reliability of rules obtained from a rough set information system by replacing strict determinacy by predicting a random variable which is a mixture  ...  of latent probabilities obtained from repeated measurements of the decision variable.  ...  Upon closer inspection, it turns out the estimation of the mixture distributions is not a pure non-parametric procedure, because the standardization to z-values is, of course, a form of parametrization  ... 
doi:10.1007/978-3-540-88425-5_23 fatcat:eerno2yabfgathaw5hbt3mp5qi

Probabilistic approach to rough sets

Wojciech Ziarko
2008 International Journal of Approximate Reasoning  
It relies on both classification knowledge and probabilistic knowledge in analysis of rules and attributes.  ...  This property makes it possible for the measure to be used to optimize and evaluate attribute-based representations through computation of probabilistic measures of attribute reduct, core and significance  ...  Acknowledgements The research reported in this article was supported in part by a research grant awarded to the author by Natural Sciences and Engineering Council of Canada.  ... 
doi:10.1016/j.ijar.2007.06.014 fatcat:vo6xxxc57bfizphk3nyzbev4wu

Unsupervised Semantic-based Aggregation of Deep Convolutional Features

Jian Xu, Chunheng Wang, Chengzuo Qi, Cunzhao Shi, Baihua Xiao
2018 IEEE Transactions on Image Processing  
classification.  ...  In this paper, we propose a simple but effective semantic-based aggregation (SBA) method. The proposed SBA utilizes the discriminative filters of deep convolutional layers as semantic detectors.  ...  in non-parametric classifiers.  ... 
doi:10.1109/tip.2018.2867104 pmid:30281422 fatcat:yr224g546fdq5fj2mqxhmlxicq

Using Discriminative Dimensionality Reduction to Visualize Classifiers

Alexander Schulz, Andrej Gisbrecht, Barbara Hammer
2014 Neural Processing Letters  
the suitability of the framework in the context of different dimensionality reduction techniques, in the context of different attention foci as concerns the visualization, and as concerns different classifiers  ...  More specifically, we use modern nonlinear dimensionality reduction (DR) techniques to project a given set of data points and their relation to the classification decision boundaries.  ...  Acknowledgements Funding from DFG under grant number HA2719/7-1 and by the CITEC center of excellence is gratefully acknowledged.  ... 
doi:10.1007/s11063-014-9394-1 fatcat:rusrun5htvhttfncbvqxyopvqu

A Review on Time Series Dimensionality Reduction

Sagar S. Badhiye
2018 Helix  
In this paper, a widespread review on the existing time series dimensionality reduction methods is given.  ...  The cumulative use of time series data has initiated a great deal of research and development attempts in the field of data mining.  ...  and helps in important data mining tasks like classification, clustering, forecasting, etc. by feature extraction, matching and computation of parametric values of the time series.  ... 
doi:10.29042/2018-3957-3960 fatcat:shg64do7nvapvnapt244p5ad6q

A review of novelty detection

Marco A.F. Pimentel, David A. Clifton, Lei Clifton, Lionel Tarassenko
2014 Signal Processing  
Table 1 1 Examples of novelty detection methods using both parametric and non-parametric probabilistic approaches.  ...  Kernel density estimators are typically classified as a non-parametric technique [33, 26, 24] as they are closely related to histogram methods, one of the earliest forms of non-parametric density estimation  ... 
doi:10.1016/j.sigpro.2013.12.026 fatcat:ha6kc4bzhbajxbo2mdyh5cw5hu

A fully automatic and robust brain MRI tissue classification method

Chris A. Cocosco, Alex P. Zijdenbos, Alan C. Evans
2003 Medical Image Analysis  
The classification procedure is robust against variability in the image quality through a non-parametric implementation: no assumptions are made about the tissue intensity distributions.  ...  A novel, fully automatic, adaptive, robust procedure for brain tissue classification from 3D magnetic resonance head images (MRI) is described in this paper.  ...  A non-parametric classifier is advisable, for the ability in the MRI data quality because it has a nonreasons presented in Section 2.2. parametric implementation: as explained in Section 2.2, the Normal-distribution  ... 
doi:10.1016/s1361-8415(03)00037-9 pmid:14561555 fatcat:ark3pj6owff6hplbu24xbuakmq

Enterprise Credit Risk Evaluation models: A Review of Current Research Trends

Ming-Chang Lee
2012 International Journal of Computer Applications  
Enterprise Credit Risk becomes important issue in financial and accounting.  ...  We found that the current research trends are necessary a method for reduction the feature subset, many hybrids SVM based model and rough model are proposed.  ...  Probabilistic neural network 4. Self-organized competition Learning vector Machine learning Support Vector Machine Soft-computing Reduction attributes 1.  ... 
doi:10.5120/6311-8643 fatcat:n4gqs53qqzdg7g3hjrzhlmjd5y

Bayesian Classification for Inspection of Industrial Products [chapter]

Petia Radeva, Marco Bressan, A. Tovar, Jordi Vitrià
2002 Lecture Notes in Computer Science  
The best results have been achieved using Bayesian classification through probabilistic modeling in a high dimensional space.  ...  We have applied a set of nonparametric and parametric methods in order to compare and evaluate their performance for this real problem.  ...  Bressan has been supported by the Secretaría de Estado de Educación y Universidades of the Ministerio de Educación, Cultura y Deportes de España.  ... 
doi:10.1007/3-540-36079-4_35 fatcat:zptqs2geeredtlcww2cbnuidwq

Automatic classification of single and hybrid power quality disturbances using Wavelet Transform and Modular Probabilistic Neural Network

S. Khokhar, A.A. Mohd. Zin, A.S. Mokhtar, M.A. Bhayo, A. Naderipour
2015 2015 IEEE Conference on Energy Conversion (CENCON)  
The classification of single and multiple power quality (PQ) disturbances is a very important task for the detection and monitoring of various faults and events in electrical power network.  ...  The PNN is found the most suitable classification tool for the classification of the PQ disturbances.  ...  Acknowledgements The authors would like to acknowledge the facilities provided by Universiti Teknologi Malaysia for the accomplishment of this work and also thankful to Quaid-e-Awam University of Engineering  ... 
doi:10.1109/cencon.2015.7409588 fatcat:wkrmzbnbdbcmhfjnzl5r2yn4da

Fine: Information embedding for document classification

Kevin M. Carter, Raviv Raich, Alfred O. Hero
2008 Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing  
The problem of document classification considers categorizing or grouping of various document types.  ...  We demonstrate that in the classification task, this information driven embedding outperforms both a standard PCA embedding and other Euclidean embeddings of the term frequency vector.  ...  In this paper, we utilize the framework presented in [4] , which we now refer to as Fisher Information Non-parametric Embedding (FINE), towards the problem of document classification.  ... 
doi:10.1109/icassp.2008.4517996 dblp:conf/icassp/CarterRH08 fatcat:ni5e2hp6zvcfxg4aapajdvi3im
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