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Coherence Functions for Multicategory Margin-based Classification Methods
2009
Journal of machine learning research
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In particular, we propose a new majorization loss function that we call the coherence function, and then devise a new multicategory margin-based boosting algorithm based on the coherence function. ...
Margin-based classification methods are typically devised based on a majorizationminimization procedure, which approximately solves an otherwise intractable minimization problem defined with the 0-l loss ...
Section 2 presents theoretical discussions of extant loss functions for multicategory margin-based classification methods. ...
dblp:journals/jmlr/ZhangJLY09
fatcat:ohh22k4r7zbtpf2pesvyi2mauu
Multicategory large margin classification methods: Hinge losses vs. coherence functions
2014
Artificial Intelligence
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Corresponding to the three hinge losses, we propose three multicategory majorization losses based on a coherence function. ...
Finally, we develop multicategory large margin classification methods by using a so-called multiclass C-loss. ...
Acknowledgements The authors would like to thank the three anonymous referees for their insightful comments on the original version of this paper. Wu ...
doi:10.1016/j.artint.2014.06.002
fatcat:6psadlz6qjbi3koxkfhmphnqye
Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data
2006
BMC Bioinformatics
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Results: In this paper, Parallel Multicategory Support Vector Machines (PMC-SVM) have been developed based on the sequential minimum optimization-type decomposition method for support vector machines ( ...
It was implemented in parallel using MPI and C++ libraries and executed on both shared memory supercomputer and Linux cluster for multicategory classification of microarray data. ...
Acknowledgements The authors are grateful to the Mississippi Center for Supercomputing Research (MCSR) for providing state-of-the-arts high performance computing facilities and excellent services for supporting ...
doi:10.1186/1471-2105-7-s4-s15
pmid:17217507
pmcid:PMC1780126
fatcat:yyj5eykzmje3zb6qu2jbgznxsu
A Fisher consistent multiclass loss function with variable margin on positive examples
2015
Electronic Journal of Statistics
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Our λ-based loss function can give unlimited weight to the positive examples without breaking the classification calibration property. ...
A large margin on positive points also facilitates faster convergence of the Sequential Minimal Optimization algorithm, leading to lower training times than other classification calibrated methods. λ-SVM ...
This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via the Federal Bureau of Investigations ...
doi:10.1214/15-ejs1073
fatcat:ef6cl4tf3favhpzclwupe2qv3y
Object-space multiphase implicit functions
2012
ACM Transactions on Graphics
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Our algorithms are inspired by machine learning algorithms for training multicategory max-margin classifiers. ...
We present multiple methods to create object-space multiphase implicit functions from existing data, including meshes and segmented medical images. ...
Acknowledgements We would like to thank Hanchuan Peng and co-authors [Peng et al. 2011] for sharing the fruit fly dataset, Tao Ju for the mouse brain dataset, Lvdi Wang for a polygonal model of the Weaire-Phelan ...
doi:10.1145/2185520.2335465
fatcat:rq62jabaone7tn4xsn6mtps7ae
Object-space multiphase implicit functions
2012
ACM Transactions on Graphics
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Our algorithms are inspired by machine learning algorithms for training multicategory max-margin classifiers. ...
We present multiple methods to create object-space multiphase implicit functions from existing data, including meshes and segmented medical images. ...
Acknowledgements We would like to thank Hanchuan Peng and co-authors [Peng et al. 2011] for sharing the fruit fly dataset, Tao Ju for the mouse brain dataset, Lvdi Wang for a polygonal model of the Weaire-Phelan ...
doi:10.1145/2185520.2185610
fatcat:76kfcrbderbd5axkxufson6neq
Support Vector Machines for Classification: A Statistical Portrait
[chapter]
2009
Msphere
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In addition, statistical properties that illuminate both advantage and limitation of the method due to its specific mechanism for classification are briefly discussed. ...
The support vector machine is a supervised learning technique for classification increasingly used in many applications of data mining, engineering, and bioinformatics. ...
This yields such model-based plug-in rules as logistic regression, LDA, QDA and other density based classification methods. ...
doi:10.1007/978-1-60761-580-4_11
pmid:20652511
fatcat:5dhalqbrxrdtfoggvek36x74im
Supervised clustering of genes
2002
Genome Biology
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methods based on single genes. ...
The identification of such gene clusters is potentially useful for medical diagnostics and may at the same time reveal insights into functional genomics. ...
p-values
Acknowledgements We thank Jane Fridlyand for providing the preprocessed NCI data. Software is available at [11] . ...
pmid:12537558
pmcid:PMC151171
fatcat:6tzvyhu2tzee7hctyxa65elyky
Unified Binary and Multiclass Margin-Based Classification
[article]
2024
arXiv
pre-print
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2023 pre-print arXiv:2311.17778v1 fatcat:56vktk2c3ndarlsjieqi3oq33y
The notion of margin loss has been central to the development and analysis of algorithms for binary classification. ...
To date, however, there remains no consensus as to the analogue of the margin loss for multiclass classification. ...
Research, 5(Oct):1225-1251, 2004.Zhihua Zhang, Michael Jordan, Wu-Jun Li, and Dit-Yan Yeung.Coherence functions for multicategory margin-based classification methods.In Artificial Intelligence and Statistics ...
arXiv:2311.17778v2
fatcat:l4njrjlsj5ecdpcaswtvsuqrqq
Robust Model-Free Multiclass Probability Estimation
2010
Journal of the American Statistical Association
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classification methods. ...
These methods often make certain assumptions on the form of probability functions or on the underlying distributions of subclasses. ...
It turns out that not all functional margin based loss (min g(f (x), y)) satisfying (0) < 0 is weighted Fisher-consistent for multicategory problems, as shown in the next proposition. Proposition 1. ...
doi:10.1198/jasa.2010.tm09107
pmid:21113386
pmcid:PMC2990887
fatcat:mdkskrxqzreypmzsopkyfptkde
MACHINE LEARNING FOR DETECTION AND DIAGNOSIS OF DISEASE
2006
Annual Review of Biomedical Engineering
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Machine learning offers a principled approach for developing sophisticated, automatic, and objective algorithms for analysis of high-dimensional and multimodal biomedical data. ...
The review describes recent developments in machine learning, focusing on supervised and unsupervised linear methods and Bayesian inference, which have made significant impacts in the detection and diagnosis ...
classification results for eight different microarray datasets Multicategory classification (%) Binary classification (%) 1 Methods BT1 BT2 L1 L2 LC PT DLBCL MC-SVM OVR 91.67 77.00 97.50 97.32 96.05 92.00 ...
doi:10.1146/annurev.bioeng.8.061505.095802
pmid:16834566
fatcat:tjxtpone55ai3nn5yapsniu4le
Class-Weighted Classification: Trade-offs and Robust Approaches
[article]
2020
arXiv
pre-print
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We address imbalanced classification, the problem in which a label may have low marginal probability relative to other labels, by weighting losses according to the correct class. ...
We define a robust risk that minimizes risk over a set of weightings and show excess risk bounds for this problem. ...
The first is class-based margin adjustment (Lin et al., 2002; Scott, 2012; Cao et al., 2019) , in which the margin parameter for the margin loss function may vary by class. ...
arXiv:2005.12914v1
fatcat:2keuzsj5djei7lyss2kwe4cl3i
Approaching Semantically-Mediated Acoustic Data Fusion
2007
MILCOM 2007 - IEEE Military Communications Conference
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of feature selection for acoustic data fusion. ...
describe our initial approaches towards establishing our hypothesis, including a survey of the enabling technologies, a description of application data (acoustic sensors, military scenario), and our new method ...
Information-based methods Compared to the traditional methods, information-based methods directly measure the information content of each individual feature. ...
doi:10.1109/milcom.2007.4455243
fatcat:xxg7gbclanhq5j6onbt4trwcrq
Deep transfer learning-based hologram classification for molecular diagnostics
[article]
2017
bioRxiv
pre-print
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., limited field of view) of traditional lens-based microcopy. ...
Combined with the developed DTL approach, LDIH could be realized as a low-cost, portable tool for point-of-care diagnostics. ...
In contrast, our ML-based approach is a reconstruction-free classification method (Fig. 2B) . ...
doi:10.1101/192559
fatcat:lqclps4azzg5bog372bzwfdcuy
Domain Adaptation with Incomplete Target Domains
[article]
2020
arXiv
pre-print
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The experimental results demonstrate the effectiveness of the proposed method. ...
We propose an Incomplete Data Imputation based Adversarial Network (IDIAN) model to address this new domain adaptation challenge. ...
a pre-defined margin value, which is used to control the distance margin between instances from different classes. ...
arXiv:2012.01606v1
fatcat:po6r6icfrneavm2dpt5isnxyz4
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