A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Filters
Support vectors selection by linear programming
2000
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium
Preserved Fulltext
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
Clustering-based identification of a piecewise affine electronic throttle model
2005
31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005.
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
Gas turbine modeling using adaptive fuzzy neural network approach based on measured data classification
2016
Mathematics-in-Industry Case Studies
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
A fuzzy systems approach in data envelopment analysis
1992
Computers and Mathematics with Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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 [20] . ...
For each cluster one could then estimate linear of nonlinear regressions. ...
doi:10.1016/0898-1221(92)90203-t
fatcat:ahxkbl25h5dk3hnvwtdgm4s34q
Music emotion recognition: The combined evidence of MFCC and residual phase
2016
Egyptian Informatics Journal
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
Authors List
2020
2020 National Conference on Communications (NCC)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
A two-stage mechanism for registration and classification of ECG using Gaussian mixture model
2009
Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2014; you can also visit the original URL.
The file type is application/pdf.
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
Page 5924 of Mathematical Reviews Vol. , Issue 90J
[page]
1990
Mathematical Reviews
Preserved Fulltext
The Internet Archive has digitized a microfilm copy of this work. It may be possible to borrow a copy for reading.
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. ...
Regularized Knowledge-Based Kernel Machine
[chapter]
2007
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
Learning to Rank by Maximizing AUC with Linear Programming
2006
The 2006 IEEE International Joint Conference on Neural Network Proceedings
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2010; you can also visit the original URL.
The file type is application/pdf.
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
Training image classifiers with similarity metrics, linear programming, and minimal supervision
2012
2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
Support vector machines
2000
SIGKDD Explorations
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
A Linear Programming Approach to Multiple Instance Learning
2020
Turkish Journal of Electrical Engineering and Computer Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
Efficiency and Performance Analysis of a Sparse and Powerful Second Order SVM Based on LP and QP
2018
International Journal of Advanced Computer Science and Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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
Nonlinear hybrid system identification with kernel models
2010
49th IEEE Conference on Decision and Control (CDC)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
The proposed method extends the framework of [1] by introducing nonparametric models based on kernel functions in order to estimate arbitrary nonlinearities without prior knowledge. ...
In comparison to the previous work of [2] , 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
« Previous
Showing results 1 — 15 out of 3,142 results