Sequential Maximum Margin Classifiers for Partially Labeled Data

2018-03-07 · Elizabeth Hou, Alfred O. Hero

In many real-world applications, data is not collected as one batch, but sequentially over time, and often it is not possible or desirable to wait until the data is completely gathered before analyzing it. Thus, we propose a framework to sequentially update a maximum margin classifier by taking advantage of the Maximum Entropy Discrimination principle. Our maximum margin classifier allows for a kernel representation to represent large numbers of features and can also be regularized with respect to a smooth sub-manifold, allowing it to incorporate unlabeled observations. We compare the performance of our classifier to its non-sequential equivalents in both simulated and real datasets.

📄 PDF Abstract BibTeX arXiv:1803.02517

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A Transductive Maximum Margin Classifier for Few-Shot Learning

2021-07-26 · Fei Pan, Chunlei Xu, Jie Guo, Yanwen Guo

Few-shot learning aims to train a classifier that can generalize well when just a small number of labeled examples per class are given. We introduce a transductive maximum margin classifier for few-shot learning (FS-TMMC…

Few-Shot Learning

Partially Observed Maximum Entropy Discrimination Markov Networks

2008-12-01 · NeurIPS 2008 12 · Jun Zhu, Eric P. Xing, Bo Zhang

Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unattainable fully annotated training data. Whi…

Structured Prediction

Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised Algorithm

2016-07-02 · Yury Maximov, Massih-Reza Amini, Zaid Harchaoui

We propose Rademacher complexity bounds for multiclass classifiers trained with a two-step semi-supervised model. In the first step, the algorithm partitions the partially labeled data and then identifies dense clusters …

Clustering

Learning Target Domain Specific Classifier for Partial Domain Adaptation

2020-08-25 · Chuan-Xian Ren, PengFei Ge, Peiyi Yang, Shuicheng Yan

Unsupervised domain adaptation~(UDA) aims at reducing the distribution discrepancy when transferring knowledge from a labeled source domain to an unlabeled target domain. Previous UDA methods assume that the source and t…

Domain Adaptation Partial Domain Adaptation Unsupervised Domain Adaptation

Direct 0-1 Loss Minimization and Margin Maximization with Boosting

2013-12-01 · NeurIPS 2013 12 · Shaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang

We propose a boosting method, DirectBoost, a greedy coordinate descent algorithm that builds an ensemble classifier of weak classifiers through directly minimizing empirical classification error over labeled training ex…

Classification General Classification