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A linear programming (LP) based method is proposed for learning from experimental data in solving the nonlinear regression and classification problems. ... LP controls both the number of basis functions in a neural network (i.e., support vector machine) and the accuracy of learning machine. ... Figure 1 . 1 Nonlinear regression: The SVs selection based on an LP algorithm Figure 2 . 2 Nonlinear Classification, i.e., Pattern Recognition: The SVs selection based on an LP learning algorithm (19 ...doi:10.1109/ijcnn.2000.861456 dblp:conf/ijcnn/KecmanH00 fatcat:4vmvoplrtbb55mzwssuus77ob4
31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005.
This modified clustering-based procedure is used to identify a PWARX model of the electronic throttle -a highly nonlinear component that regulates air inflow to the engine of a car. ... We significantly reduce the computational complexity of the classification algorithm for finding the complete polyhedral partition of the model domain. ... on clustering. ...doi:10.1109/iecon.2005.1568900 fatcat:4pqpao6iffad7iokjci4xc73pu
On the other side, by focusing on the membership functions for residual generator to get consistent settings based on the used data structure classification and selection, where the main goal is to obtain ... On the other side, the physical model of a gas turbine can be obtained by dynamic simulations in the conception step, or based on real plant data of these types of machine in exploitation. ... This allows to develop a global model based on fuzzy clustering method using algorithms based on fuzzy inference systems for classification of real data of the examined gas turbine. ...doi:10.1186/s40929-016-0006-3 fatcat:szjj7ld7pva53ichabikr25k5y
Almtract~The use of fuzzy set-theoretlc measures is explored here in the context of data enve]opment analysis, which utilizes a nonparametrlc approach to measure efficiency. ... Finally, the decision maker may choose between a class of nonlinear membership functions by applying the criterion of minimal predictive error  . ... For each cluster one could then estimate linear of nonlinear regressions. ...doi:10.1016/0898-1221(92)90203-t fatcat:ahxkbl25h5dk3hnvwtdgm4s34q
Emotion recognition in music considers the emotions namely anger, fear, happy, neutral and sad. ... The residual phase is defined as the cosine of the phase function of the analytic signal derived from the linear prediction (LP) residual and also it is demonstrated that the residual phase signal contains ... by minimizing the mean squared prediction error over the analysis frame. ...doi:10.1016/j.eij.2015.05.004 fatcat:cocuo6xstjftfp4x5uibavvugu
2020 National Conference on Communications (NCC)
Arrhythmia and Congestive Heart Failure in ECG Kernelized Graph-based Multi-view Clustering on High Dimensional Data Low-Rank Kernelized Graph-based Clustering Using Multiple Views Classification ... Elastic Optical Networks with Minimal Disruption Uniformly Most Powerful CFAR Test for Pareto-Target Detection in Pareto Distributed Clutter Goutam Saha A Fusion Based Classification of Normal, ...doi:10.1109/ncc48643.2020.9056032 fatcat:tsdhbqblujfwlgf4ojr6ftqdhe
In first stage, pre-processing that includes re-sampling, QRS detection, linear prediction (LP) model estimation, residual error signal computation and principal component analysis (PCA) has been used ... A set of 12 features explaining 99.7% of the data variability is obtained using PCA from residual error signals for GMM based classification. ... Sarbajit Pal for their valuable support in carrying out this research work. Additionally, we are extremely grateful to VECC, Kolkata for funding this work. ...doi:10.1016/j.patcog.2009.02.008 fatcat:3jozwvsfcvf3hbymrv5wbqpqmq
Let d* =d(f*, y7, y3,---, yz.) be the optimal clustering in the sense that d* minimizes L(d), and let d, = da( Snr V\” YS, °°, yi”) be the optimal sample clustering which minimizes the sample loss L,,( ... The aim of the clustering procedure in this paper is to obtain the few cluster descriptors instead of the many data points. ...
Lecture Notes in Computer Science
This paper presents a knowledge-based kernel classification model for binary classification of sets or objects with prior knowledge. ... The prior knowledge is in the form of multiple polyhedral sets belonging to one or two classes, and it is introduced as additional constraints into a regularized knowledge-based optimization problem. ... Conclusion In this paper, a binary classification model called the nonlinear classification Tikhonov regularization knowledge-based kernel machine (NT R KKM) is described. ...doi:10.1007/978-3-540-72584-8_23 fatcat:nv5txgbykbff3bujs34xgfa6pe
Area Under the ROC Curve (AUC) is often used to evaluate ranking performance in binary classification problems. ... Our ranking algorithm outperforms SVMs in both AUC and classification performance when using RBF kernels, but curiously not with polynomial kernels. ... Therefore an LP is constructed to minimize some error term corresponding to the number of incorrect orderings. ...doi:10.1109/ijcnn.2006.246669 dblp:conf/ijcnn/AtamanSZ06 fatcat:iqnscqkpgvb73nomkef75iuq3e
Our optimization problem uses dot products in the feature space exclusively, and therefore can be extended to non-linear kernel functions in the transductive setting. ... Image classification is a classical computer vision problem with applications to semantic image annotation, querying, and indexing. ... ACKNOWLEDGEMENTS We would like to thank Andrew Bolstad at MIT Lincoln Laboratory for all the help, advice, and good ideas he has given us in regard to convex optimization for sparse regularization techniques ...doi:10.1109/acssc.2012.6489386 dblp:conf/acssc/NiPBB12 fatcat:q4nxsspsffgt3b6qyuq4ws6pb4
While this overview is not comprehensive, it does provide resources for those interested in further exploring SVMs. ... The classification problem is used to investigate the basic concepts behind SVMs and to examine their strengths and weaknesses from a data mining perspective. ... The resulting QP does this by minimizing the error and minimizing the 2-norm of w. ...doi:10.1145/380995.380999 fatcat:mwuco6xjejhrjmcj7o4clqy6da
Our experiments with 10 instance-dissimilarity based data representations verify the effectiveness of the proposed MIL framework. ... Proposed 11 mathematical models can be solved efficiently in polynomial time. 12 ... first linear programming based classification approach in MIL. ...doi:10.3906/elk-2009-144 fatcat:c77nrs7o2zhx7o6ahhbvhlv56e
as the LP one. ... Despite this heavy test cost reduction, its classification accuracy is very similar to the most powerful QP SVM while being very simple to be produced. ... We then run LP (in VLPSVM fashion) on this SVs set as this LP will impose a co-efficient vector carrying weights of these SVs patterns to minimize the objective function while maintaining classification ...doi:10.14569/ijacsa.2018.090244 fatcat:epnusw4cardtvfowjcrdgkny5i
The proposed method extends the framework of  by introducing nonparametric models based on kernel functions in order to estimate arbitrary nonlinearities without prior knowledge. ... In comparison to the previous work of  , which also dealt with unknown nonlinearities, the new algorithm assumes the form of an unconstrained nonlinear continuous optimization problem, which can be ... Nonlinear models based on kernel functions are introduced in these algorithms to be able to estimate unknown nonlinearities. ...doi:10.1109/cdc.2010.5718011 dblp:conf/cdc/LauerBV10 fatcat:m4h3gn4ycrbpfcj7gf3khhnhqy
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