A Kernel Method for the Optimization of the Margin Distribution

Aiolli, Fabio; Martino, Giovanni San; Sperduti, Alessandro

doi:10.1007/978-3-540-87536-9_32

Cited by 38 publications

(38 citation statements)

References 5 publications

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“…The kernel machines (SVM and KOMD) were validated using the same subset of data to obtain a fair comparison. The performance of SVM and KOMD was similar in most of the cases, thus confirming the findings in [7].…”

Section: Smo Projected Gradient Descent Generalized Mkl (Spg-gmkl)supporting

confidence: 88%

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EasyMKL: a scalable multiple kernel learning algorithm

2015

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Section: Smo Projected Gradient Descent Generalized Mkl (Spg-gmkl)supporting

confidence: 88%

“…In [7] a game theoretic interpretation as a two-player zero-sum game has been proposed for the problem of margin maximization in a classification task. Specifically, the classification task has been split into two phases.…”

Section: Playing With Margin and The Komd Algorithmmentioning

confidence: 99%

EasyMKL: a scalable multiple kernel learning algorithm

2015

Self Cite

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“…Table 1 shows some experimental results of comparing LDM to SVM, where it can be seen that LDM is significantly better on more than half of the experimental datasets and never worse than SVM. Such a simple implementation of large margin distribution learning also exhibits superior performance to many other related methods [1,6,11] in experiments [21].…”

Section: A Simple Implementation Of Large Margin Distribution Learningmentioning

confidence: 84%

“…Reyzin and Schapire [12] suggested to maximize the average or median margin, and there are also efforts on maximizing the average margin or weighted combination margin [1,6,11]. These arguments, however, are all heuristics without theoretical justification.…”

Section: The Long March Of Margin Theory For Boostingmentioning

confidence: 99%

Large Margin Distribution Learning

Zhou

2014

Advanced Information Systems Engineering

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Abstract. Support vector machines (SVMs) and Boosting are possibly the two most popular learning approaches during the past two decades. It is well known that the margin is a fundamental issue of SVMs, whereas recently the margin theory for Boosting has been defended, establishing a connection between these two mainstream approaches. The recent theoretical results disclosed that the margin distribution rather than a single margin is really crucial for the generalization performance, and suggested to optimize the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. Inspired by this recognition, we advocate the large margin distribution learning, a promising research direction that has exhibited superiority in algorithm designs to traditional large margin learning.

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“…In this work, a custom version of EasyMKL [1] was implemented as it achieves high performance at a low computational cost. EasyMKL is based on the Kernel method for the Optimization of the Margin Distribution (KOMD) [2] and focuses on optimizing a linear combination of kernels:…”

Section: Pathway Induced Multiple Kernel Learningmentioning

confidence: 99%

PIMKL: Pathway-Induced Multiple Kernel Learning

et al. 2019

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Reliable identification of molecular biomarkers is essential for accurate patient stratification. While state-of-the-art machine learning approaches for sample classification continue to push boundaries in terms of performance, most of these methods are not able to integrate different data types and lack generalization power, limiting their application in a clinical setting. Furthermore, many methods behave as black boxes, and we have very little understanding about the mechanisms that lead to the prediction. While opaqueness concerning machine behavior might not be a problem in deterministic domains, in health care, providing explanations about the molecular factors and phenotypes that are driving the classification is crucial to build trust in the performance of the predictive system. We propose Pathway-Induced Multiple Kernel Learning (PIMKL), a methodology to reliably classify samples that can also help gain insights into the molecular mechanisms that underlie the classification. PIMKL exploits prior knowledge in the form of a molecular interaction network and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm, an approach that has demonstrated excellent performance in different machine learning applications. After optimizing the combination of kernels to predict a specific phenotype, the model provides a stable molecular signature that can be interpreted in the light of the ingested prior knowledge and that can be used in transfer learning tasks.

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A Kernel Method for the Optimization of the Margin Distribution

Cited by 38 publications

References 5 publications

EasyMKL: a scalable multiple kernel learning algorithm

EasyMKL: a scalable multiple kernel learning algorithm

Large Margin Distribution Learning

PIMKL: Pathway-Induced Multiple Kernel Learning

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