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http://www.stat.purdue.edu/~yuzhu/stat598m3/Papers/NewSVM.pdf
New Support Vector Algorithms ... We propose a new class of support vector algorithms for regression and classi” cation. In these algorithms, a parameterºlets one effectively con-trol the number of support vectors. While this can be useful in its own right, the parameterization has the additional bene” t of enabling us to
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.6040
BibTeX @MISC{Schölkopf00newsupport, author = {Bernhard Schölkopf and Alex J. Smola and Robert C. Williamson and Peter L. Bartlett}, title = {New Support Vector Algorithms}, year = …
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.94.2928
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter º lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of …
https://dl.acm.org/citation.cfm?id=1139691
We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter lets one effectively control the …Cited by: 3132
https://direct.mit.edu/books/book/1821/Learning-with-KernelsSupport-Vector-Machines
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept …
http://alex.smola.org/papers/2000/SchSmoWilBar00.pdf
New Support Vector Algorithms 1209 Figure 1: In SV regression, a desired accuracy "is specified a priori. It is then attempted to fit a tube with radius "to the data. The trade-off between model complexity and points lying outside the tube (with positive slack variables »)is determined by minimizing the expression 1.5.
http://www.kernel-machines.org/publications/SchSmoWilBar98/bibliography_exportForm
New Support Vector Algorithms. Books. Software. Annual Workshop. JMLR. Links. Tutorials. News ... A specific renderer to export bibliographical references in BiBTeX format. Note: If the output encoding of the BiBTeX renderer is not unicode capable, all non-ASCII characters will be resolved to LaTeX entities.
https://www.researchgate.net/publication/2611423_Shrinking_the_Tube_A_New_Support_Vector_Regression_Algorithm
A new algorithm for Support Vector regression is described. For a priori chosen , it automatically adjusts a flexible tube of minimal radius to the data such that at most a fraction of the data ...
https://dl.acm.org/citation.cfm?id=1660541
In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an arbitrary shape is demonstrated. This can be useful in many cases, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value x.Cited by: 100
https://www.researchgate.net/publication/319765351_A_New_Support_Vector_Finder_Method_Based_on_Triangular_Calculations_and_K-means_Clustering
In this paper, a new support vector finder method, based on triangle, has been presented, which finds support vectors based on triangular calculations, like calculating triangle angles, area and ...
http://www.stat.purdue.edu/~yuzhu/stat598m3/Papers/NewSVM.pdf
New Support Vector Algorithms 1209 Figure 1: In SV regression, a desired accuracyeis speci” ed a priori.It is then attempted to ” t a tube with radiuseto the data.The trade-off between model
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.94.2928
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter º lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.4373
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We describe a new class of Support Vector algorithms for regression and classification. In these algorithms, a parameter lets one effectively control the number of Support Vectors. While this can be useful in its own right, the parametrization has the additional benefit of enabling us to eliminate one of the other ...
https://dl.acm.org/citation.cfm?id=1139691
We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter lets one effectively control the number of support vectors. While this can be...Cited by: 3132
https://direct.mit.edu/books/book/1821/Learning-with-KernelsSupport-Vector-Machines
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks.
https://www.researchgate.net/publication/2611423_Shrinking_the_Tube_A_New_Support_Vector_Regression_Algorithm
A new algorithm for Support Vector regression is described. For a priori chosen , it automatically adjusts a flexible tube of minimal radius to the data such that at most a fraction of the data ...
https://dl.acm.org/citation.cfm?id=1660541
In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an arbitrary shape is demonstrated. This can be useful in many cases, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value x.Cited by: 100
http://www.kernel-machines.org/publications/SchSmoWilBar98/bibliography_exportForm
New Support Vector Algorithms. Books. Software. Annual Workshop. JMLR. Links. Tutorials. News ... A specific renderer to export bibliographical references in BiBTeX format. Note: If the output encoding of the BiBTeX renderer is not unicode capable, all non-ASCII characters will be resolved to LaTeX entities.
https://www.sciencedirect.com/science/article/pii/S0893608009002019
Like the previous v-SVM, the proposed new support vector algorithms with parametric insensitive/margin model have the advantage of using the parameter 0 ≤ v …Cited by: 100
https://www.researchgate.net/publication/319765351_A_New_Support_Vector_Finder_Method_Based_on_Triangular_Calculations_and_K-means_Clustering
In this paper, a new support vector finder method, based on triangle, has been presented, which finds support vectors based on triangular calculations, like calculating triangle angles, area and ...
https://en.wikipedia.org/wiki/Support_vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss.
https://www.cs.cornell.edu/people/tj/svm_light/index.html
SVM light is an implementation of Vapnik's Support Vector Machine [Vapnik, 1995] for the problem of pattern recognition, for the problem of regression, and for the problem of learning a ranking function. The optimization algorithms used in SVM light are described in [Joachims, 2002a]. [Joachims, 1999a]. The algorithm has scalable memory ...
https://asmedigitalcollection.asme.org/energyresources/article/136/2/024502/373129/A-New-Support-Vector-Machine-and-Artificial-Neural
Stuck pipe is known to be influenced by drilling fluid properties and other parameters, such as the characteristics of rock formations. In this paper, we develop a support-vector-machine (SVM) based model to predict stuck pipe during drilling design and operations. To develop the model, we use a dataset, including stuck and nonstuck cases.
https://link.springer.com/chapter/10.1007%2F978-3-642-02298-2_89
Support Vector Machines (SVMs) have been dominant learning techniques for more than ten years, and mostly applied to supervised learning problems. Recently nice results are obtained by two-class unsupervised classification algorithms where the optimization problems based on Bounded C -SVMs, Bounded ν -SVMs and Lagrangian SVMs respectively are ...
https://arxiv.org/abs/1710.05895
Classifiers and rating scores are prone to implicitly codifying biases, which may be present in the training data, against protected classes (i.e., age, gender, or race). So it is important to understand how to design classifiers and scores that prevent discrimination in predictions. This paper develops computationally tractable algorithms for designing accurate but fair support vector ...
https://infoscience.epfl.ch/record/33301
Humans have the ability to learn. Having seen an object we can recognise it later. We can do this because our nervous system uses an efficient and robust visual processing and capabilities to learn from sensory input. On the other hand, designing algorithms to learn from visual data is a difficult task. More than fifty years ago, Rosenblatt proposed the perceptron algorithm. The perceptron ...
https://journals.sagepub.com/doi/abs/10.1177/0954406213477777
In the present work, a multi-fault classification of gears has been attempted by the support vector machine learning technique using the vibration data in time domain. A proper utilization of the support vector machine is based on the selection of support vector machine parameters.
https://easychair.org/publications/preprint/g6d1
The implementation of the proposed learning algorithms is computationally easy and fast. We apply the new algorithms to analyze the data arising from a gene expression study. Keyphrases: Classification, graphical model, network structure, Support Vector Machine
https://academic.oup.com/jamia/article/19/5/917/723897
In 2011 Matwin et al published a letter to JAMIA entitled ‘Performance of SVM and Bayesian classifiers on the systematic review classification task’.1 This letter continued a discussion on the relative benefits of using support vector machine (SVM) and Bayesian techniques for performing systematic reviews.2–4 In particular, it was suggested that the running time of algorithms must be ...
http://www.eurekaselect.com/openurl/content.php?genre=article&issn=0929-8665&volume=17&issue=12&spage=1473
Prediction of Apoptosis Protein Locations with Genetic Algorithms and Support Vector Machines Through a New Mode of Pseudo Amino Acid Composition. ... Prediction of Apoptosis Protein Locations with Genetic Algorithms and Support Vector Machines Through a New Mode of Pseudo Amino Acid Composition. ... BibTeX. Text. Content: Citation Only ...
https://books.google.com/books/about/Learning_to_Classify_Text_Using_Support.html?id=GNTZjXHjB7kC
Apr 30, 2002 · Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a …4/5(1)
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/7496/1/A-new-incremental-learning-algorithm-based-on-Support-Vector-Machines/10.1117/12.832552.full
30 October 2009 A new incremental learning algorithm based on Support Vector Machines. Zuying Miao; ... we first analyzed the possible change of support vector set after new samples are added, then presented a new support vector machine incremental learning algorithm. ... Efficient inference algorithms for hybrid dynamic Bayesian networks (HDBN ...
https://github.com/AFAgarap/wisconsin-breast-cancer/
On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset. This paper was presented at the 2nd International Conference on Machine Learning and Soft Computing (ICMLSC) in Phu Quoc Island, Vietnam last February 2-4, 2018.
https://arxiv.org/abs/1611.07054
Nov 21, 2016 · Survival analysis is a fundamental tool in medical research to identify predictors of adverse events and develop systems for clinical decision support. In order to leverage large amounts of patient data, efficient optimisation routines are paramount. We propose an efficient training algorithm for the kernel survival support vector machine (SSVM). We directly optimise the primal objective ...
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