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Large margin classification using the perceptron algorithm
1998
Proceedings of the eleventh annual conference on Computational learning theory - COLT' 98
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Like Vapnik's maximal-margin classifier, our algorithm takes advantage of data that are linearly separable with large margins. ...
We introduce and analyze a new algorithm for linear classification which combines Rosenblatt's perceptron algorithm with Helmbold and Warmuth's leave-one-out method. ...
However, as we shall see, when perceptrons are used together with kernels, the excess in memory and computation is really quite minimal. 2. ...
doi:10.1145/279943.279985
dblp:conf/colt/FreundS98
fatcat:kwakvsczu5flxkun2pbaqqmggy
MARGINAL PERCEPTRON FOR NON-LINEAR AND MULTI CLASS CLASSIFICATION
2020
Zenodo
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The proposed classification algorithm implements margin in classical perceptron algorithm, to reduce generalized errors by maximizing margin of separating hyperplane. ...
Algorithm uses the same updation rule with the perceptron, to converge in a finite number of updates to solutions, possessing any desirable fraction of the margin. ...
Such algorithms include Relaxed Online Maximum Margin Algorithm (ROMMA) [4] , Approximate Large Margin Algorithm (ALMA) [5] . , k x [6] . ...
doi:10.5281/zenodo.4271590
fatcat:btc7kqlvxnabplzfcmj4pes6z4
A NOTE ON A LARGE MARGIN PERCEPTRON ALGORITHM
2006
Information Technology and Control
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In order to achieve better generalization properties the additional use of an iterative large margin perceptron algorithm is investigated. ...
The importance of classification algorithms in the context of risk assessment is briefly explained. ...
Hence in this paper the additional use of an iterative large margin perceptron algorithm as proposed by Krauth and Mezard, cf. [13] is treated. ...
doi:10.5755/j01.itc.35.3.11764
fatcat:aeepzkootfcfvgdwepy53dp6s4
Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain
[chapter]
2010
Lecture Notes in Computer Science
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Both use the simple but very efficient perceptron algorithm as underlying classifier. ...
In this paper we evaluate the performance of multilabel classification algorithms on two classification tasks related to documents of the EUR-Lex database of legal documents of the European Union. ...
Acknowledgements This work was supported by the EC 6th framework project ALIS (Automated Legal Information System). ...
doi:10.1007/978-3-642-12837-0_11
fatcat:rv6eacprzrfklgrin7f4k7egjq
Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice
[chapter]
2006
Lecture Notes in Computer Science
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Both our approaches not only are simpler than existing algorithms, but also have a stronger connection to the large-margin bounds. ...
We derive novel largemargin bounds of common error functions, such as the classification error and the absolute error. ...
Conclusion We proposed a thresholded ensemble model for ordinal regression, and defined margins for the model. Novel large-margin bounds of common error functions were proved. ...
doi:10.1007/11894841_26
fatcat:zw6h27h7sraepmb2a5jgh77phq
Ranking and Reranking with Perceptron
2005
Machine Learning
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We apply the new perceptron algorithms to the parse reranking and machine translation reranking tasks, and study the performance of reranking by employing various definitions of the margins. ...
Compared to the approach of using pairwise objects as training samples, the new algorithms reduces the data complexity and training time. ...
Acknowledgements We thank anonymous reviewers for useful comments. ...
doi:10.1007/s10994-005-0918-9
fatcat:5uouaaawafcqvlsgrfybppb4tu
Links between perceptrons, MLPs and SVMs
2004
Twenty-first international conference on Machine learning - ICML '04
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We propose to study links between three important classification algorithms: Perceptrons, Multi-Layer Perceptrons (MLPs) and Support Vector Machines (SVMs). ...
We first study ways to control the capacity of Perceptrons (mainly regularization parameters and early stopping), using the margin idea introduced with SVMs. ...
Acknowledgments This research has been carried out in the framework of the Swiss NCCR project (IM)2, and in the framework of the PASCAL European Network of Excellence, funded by the Swiss OFES. ...
doi:10.1145/1015330.1015415
dblp:conf/icml/CollobertB04
fatcat:akqtpkj2hbhq7f4gu7po35e5su
Flexible Margin Selection for Reranking with Full Pairwise Samples
[chapter]
2005
Lecture Notes in Computer Science
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Perceptron like large margin algorithms are introduced for the experiments with various margin selections. ...
Compared to the previous perceptron reranking algorithms, the new algorithms use full pairwise samples and allow us to search for margins in a larger space. ...
Acknowledgments We thank Michael Collins for help concerning the data set. ...
doi:10.1007/978-3-540-30211-7_47
fatcat:dofawe5kpvgkxj2qan5gxl53s4
Perceptron like Algorithms for Online Learning to Rank
[article]
2016
arXiv
pre-print
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Perceptron is a classic online algorithm for learning a classification function. ...
A modern perspective on perceptron for classification is that it is simply an instance of online gradient descent (OGD), during mistake rounds, using the hinge loss function. ...
Acknowledgments We gratefully acknowledge the support of NSF under grant IIS-1319810. We also thank Prateek Jain for pointing out the relevant question on perceptron bound for NDCG cut-off at k m. ...
arXiv:1508.00842v4
fatcat:j6ntwore6zfndoohsvj2ampysy
Highly Scalable and Provably Accurate Classification in Poincare Balls
[article]
2021
arXiv
pre-print
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The gist of our approach is to focus on Poincar\'e ball models and formulate the classification problems using tangent space formalisms. ...
Our results include a new hyperbolic and second-order perceptron algorithm as well as an efficient and highly accurate convex optimization setup for hyperbolic support vector machine classifiers. ...
For large margin classification, we further required that the learnt w achieves the largest possible margin, max w∈TpB n min i∈[N ] y i h w,p (x i )d(x i , H w,p ). (10)
A. ...
arXiv:2109.03781v3
fatcat:hpicmn6zyjfxblf5syfpofm5em
Classification and Ranking Approaches to Discriminative Language Modeling for ASR
2013
IEEE Transactions on Audio, Speech, and Language Processing
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We formulate this both as a classification and a ranking problem and employ the perceptron, the margin infused relaxed algorithm (MIRA) and the support vector machine (SVM). ...
Using the MIRA or SVM does not lead to any further improvement over the perceptron but the use of ranking as opposed to classification leads to a 0.4% reduction in word error rate (WER) which is statistically ...
ACKNOWLEDGMENT The authors would like to thank Ebru Arısoy for the baseline DLM setup. ...
doi:10.1109/tasl.2012.2221461
fatcat:lsqerv2g3zhczcklfu5hcnce4u
Multi-class protein fold recognition using adaptive codes
2005
Proceedings of the 22nd international conference on Machine learning - ICML '05
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In order to compare against widely used methods in protein sequence analysis, we also test nearest neighbor approaches based on the PSI-BLAST algorithm. ...
We develop a novel multi-class classification method based on output codes for the problem of classifying a sequence of amino acids into one of many known protein structural classes, called folds. ...
Acknowledgments We would like to thank Thorsten Joachims for helpful suggestions on the implementation of SVM-Struct and Asa Ben-Hur for helpful comments on the manuscript. ...
doi:10.1145/1102351.1102393
dblp:conf/icml/IeWNL05
fatcat:x4pr34j2wvc5lce2dbjnhngdwe
Perceptron Learning of Modified Quadratic Discriminant Function
2011
2011 International Conference on Document Analysis and Recognition
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Recent advances justify the efficacy of minimum classification error criteria in learning MQDF (MCE-MQDF). We provide an alternative choice to MCE-MQDF based on the Perceptron learning (PL-MQDF). ...
For better generalization performance, we propose a new dynamic margin regularization. ...
Active Set Technique We employ active set technique to speed up the training process of Perceptron algorithm. If a instance invokes a classification error or margin error, we say it "active". ...
doi:10.1109/icdar.2011.204
dblp:conf/icdar/SuLZ11
fatcat:ppwq33rwgbfd5kepk3kt7dg5ya
A learning rule for very simple universal approximators consisting of a single layer of perceptrons
2008
Neural Networks
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We show that this assumption is not true, by exhibiting a simple learning algorithm for parallel perceptrons -the parallel delta rule ( p-delta rule). ...
In spite of their simplicity, such circuits can compute any Boolean function if one views the majority of the binary perceptron outputs as the binary output of the parallel perceptron, and they are universal ...
In our setting we use the clear margin to stabilize the output of the parallel perceptron. ...
doi:10.1016/j.neunet.2007.12.036
pmid:18249524
fatcat:75kvkyccgnfqhpyib6u3dk754e
Approximate maximum margin algorithms with rules controlled by the number of mistakes
2007
Proceedings of the 24th international conference on Machine learning - ICML '07
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classification with maximum margin. ...
We present a family of Perceptron-like algorithms with margin in which both the "effective" learning rate, defined as the ratio of the learning rate to the length of the weight vector, and the misclassification ...
Our purpose here is to address the maximal margin classification problem in the context of Perceptron-like algorithms which, however, differ from the above variants in that the "effective" learning rate ...
doi:10.1145/1273496.1273610
dblp:conf/icml/TsampoukaS07
fatcat:ikcabgm55rbddlnrkefyislvsi
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