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Related Experiment Video

Updated: May 19, 2026

Functional Magnetic Resonance Imaging (fMRI) of the Visual Cortex with Wide-View Retinotopic Stimulation

Functional Magnetic Resonance Imaging (fMRI) of the Visual Cortex with Wide-View Retinotopic Stimulation

Published on: December 8, 2023

Insights from classifying visual concepts with multiple kernel learning.

Alexander Binder¹, Shinichi Nakajima, Marius Kloft

¹Machine Learning Group, Berlin Institute of Technology, Berlin, Germany. alexander.binder@tu-berlin.de

|September 1, 2012

Summary

This summary is machine-generated.

Non-sparse Multiple Kernel Learning (MKL) improves concept recognition in computer vision. This study compares non-sparse MKL against sparse MKL and sum-kernel SVMs, showing its effectiveness on benchmark datasets.

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Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

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Published on: December 8, 2023

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

Computer Vision
Machine Learning
Pattern Recognition

Background:

Combining image features is standard for concept recognition.
Optimal fusion of kernel functions remains a challenge.
Multiple Kernel Learning (MKL) addresses kernel fusion but sparse variants often underperform.

Purpose of the Study:

Apply a novel non-sparse MKL variant to computer vision concept recognition.
Evaluate benefits and limitations of non-sparse MKL.
Compare non-sparse MKL against sparse MKL and sum-kernel SVM.

Main Methods:

Utilized a recently developed non-sparse MKL approach.
Applied methods to PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation datasets.
Compared performance against sparse MKL and sum-kernel SVM.

Main Results:

Non-sparse MKL demonstrates effectiveness in state-of-the-art concept recognition.
Empirical results on PASCAL VOC and ImageCLEF datasets are reported.
Insights into the performance trade-offs of different MKL strategies are provided.

Conclusions:

Non-sparse MKL offers a competitive alternative to existing methods.
The study provides valuable empirical evidence for MKL applications in computer vision.
Dataset and kernel matrices are publicly available for further research.