Structured variable selection in support vector machines

Wu, Seongho; Zou, Hui; Yuan, Ming

doi:10.1214/07-ejs125

Cited by 9 publications

(7 citation statements)

References 29 publications

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“…Structured Perceptron [6] and structured SVM [7] based approaches only require efficiently compute the arg max, unlike CRFs which require computing many other marginals and partition function. These approaches are non-probabilistic, and they utilize a simple form for the score:…”

Section: Hmms Memms Crfs and Structured Perceptronmentioning

confidence: 99%

Structured Prediction in NLP -- A survey

Chauhan¹,

Biyani²,

P.³

et al. 2021

Preprint

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Over the last several years, the field of Structured prediction in NLP has had seen huge advancements with sophisticated probabilistic graphical models, energy-based networks, and its combination with deep learning-based approaches. This survey provides a brief of major techniques in structured prediction and its applications in the NLP domains like parsing, sequence labeling, text generation, and sequence to sequence tasks. We also deep-dived into energy-based and attention-based techniques in structured prediction, identified some relevant open issues and gaps in the current state-of-the-art research, and have come up with some detailed ideas for future research in these fields.

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Section: Hmms Memms Crfs and Structured Perceptronmentioning

confidence: 99%

Structured Prediction in NLP -- A survey

Chauhan¹,

Biyani²,

P.³

et al. 2021

Preprint

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“…Various variable selection methods were studied in recent years to encourage sparsity in SVMs (Cortes & Vapnik, 1995). References include Zhu et al (2003), Wu et al (2008) and Park et al (2012). Our work can be viewed as a penalized weighted SVM.…”

Section: Introductionmentioning

confidence: 99%

On sparse representation for optimal individualized treatment selection with penalized outcome weighted learning

Song

Kosorok

Zeng

et al. 2015

Stat

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As a new strategy for treatment which takes individual heterogeneity into consideration, personalized medicine is of growing interest. Discovering individualized treatment rules (ITRs) for patients who have heterogeneous responses to treatment is one of the important areas in developing personalized medicine. As more and more information per individual is being collected in clinical studies and not all of the information is relevant for treatment discovery, variable selection becomes increasingly important in discovering individualized treatment rules. In this article, we develop a variable selection method based on penalized outcome weighted learning through which an optimal treatment rule is considered as a classification problem where each subject is weighted proportional to his or her clinical outcome. We show that the resulting estimator of the treatment rule is consistent and establish variable selection consistency and the asymptotic distribution of the estimators. The performance of the proposed approach is demonstrated via simulation studies and an analysis of chronic depression data.

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“…Exploring the relationship between features is not new. Recently in structured feature selection, supervised learning algorithms have been explored for data sets where features have some natural "structure" relationships [27,28,29,30,31]. For example Yuan and Lin explored the situation where features may be naturally partitioned into groups and studied the regression problem of grouped features using a technique called grouped Lasso [28].…”

Section: Introductionmentioning

confidence: 99%

“…Another possible type of structure relationship of features is a hierarchical relation (i.e. a directed acyclic graph defined on features) and that has been explored in [27,30]. In [29], both group structure and hierarchical relation have been studied in a unified framework.…”

Section: Introductionmentioning

confidence: 99%

Aligned Graph Classification with Regularized Logistic Regression

Quanz¹,

Huan²

2009

Proceedings of the 2009 SIAM International Conference on Data Mining

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Data with intrinsic feature relationships are becoming abundant in many applications including bioinformatics and sensor network analysis. In this paper we consider a classification problem where there is a fixed and known binary relation defined on the features of a set of multivariate random variables. We formalize such a problem as an aligned graph classification problem. By incorporating this feature relationship in the learning process we aim to obtain improved classification performance over conventional learning that does not consider the additional information of the feature relationship. To incorporate the feature relationship, we extend logistic regression and use a regularization term that includes the normalized Laplacian of the graph, similar to the L2 regularization, deriving a modified optimization problem and solution. We demonstrate the effectiveness of our method and compare it to other methods using simulated and real data sets.

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Structured variable selection in support vector machines

Cited by 9 publications

References 29 publications

Structured Prediction in NLP -- A survey

Structured Prediction in NLP -- A survey

On sparse representation for optimal individualized treatment selection with penalized outcome weighted learning

Aligned Graph Classification with Regularized Logistic Regression

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