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Example-based Support Vector Machine for Drug Concentration Analysis

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Transfer Learning by Sharing Support Vectors

Pascal Fua, Carlos Joaquin Becker, Vitaly Henry Ablavsky

Machine Learning techniques play an increasingly vital role in the analysis of Biomedical imagery, as in all other areas of Computer Vision. However, in this specific context, they suffer from the fact that experimental conditions and protocols change ofte ...

2012

RANSAC-based Enhancement in Drug Concentration Prediction Using Support Vector Machine

Giovanni De Micheli, Alena Simalatsar, Wenqi You Dubout

Training Support Vector Machines (SVMs) to predict drugs concentrations is often difficult because of the high level of noise in the training data, due to various kinds of measurement errors. We apply RANdom SAmple Consensus (RANSAC) algorithm in this pape ...

2012

Personalized Modeling for Drug Concentration Prediction Using Support Vector Machine

Giovanni De Micheli, Wenqi You Dubout

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propos ...

2011

Nano-Sensor and Circuit Design for Anti-Cancer Drug Detection

Giovanni De Micheli, Sandro Carrara, Camilla Baj-Rossi, Seyedeh Sara Ghoreishizadeh

The development of new technologies to monitor drugs concentration directly in patient’s blood is absolutely required to succeed in personalized drug therapies. In this study, Etoposide - a well-known anti-cancer drug - has been chosen as model for cyclic ...

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It is All About Proteases: From Drug Delivery to In Vivo Imaging and Photomedicine

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Clinical studies provide overwhelming evidence for the importance of proteolytic imbalance and the upregulation of diverse protease classes in diseases such as cancer and arthritis. While the complex nature of proteolytic networks has hampered the developm ...

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On-line independent support vector machines

Barbara Caputo

Support vector machines (SVMs) are one of the most successful algorithms for classification. However, due to their space and time requirements, they are not suitable for on-line learning, that is, when presented with an endless stream of training observati ...

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DCE-MRI data analysis for cancer area classification

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OBJECTIVES: The paper aims at improving the support of medical researchers in the context of in-vivo cancer imaging. Morphological and functional parameters obtained by dynamic contrast-enhanced MRI (DCE-MRI) techniques are analyzed, which aim at investiga ...

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SimpleMKL

Multiple kernel learning (MKL) aims at simultaneously learning a kernel and the associated predictor in supervised learning settings. For the support vector machine, an efficient and general multiple kernel learning algorithm, based on semi-infinite linear ...

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Ensembles of SVMs using an Information Theoretic Criterion

Jean-Philippe Thiran, Julien Meynet

Training Support Vector Machine can become very challenging in large scale problems. Training several lower complexity SVMs on local subsets of the training set can significantly reduce the training complexity and also improve the classification performanc ...

2008

On-line Independent Support Vector Machines for Cognitive Systems

Barbara Caputo, Jie Luo

Learning from experience and adapting to changing stimuli are fundamental capabilities for artificial cognitive systems. This calls for on-line learning methods able to achieve high accuracy while at the same time using limited computer power. Research on ...

IDIAP2007