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Artificial Intelligence
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
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IEEE Transactions on Pattern Analysis and Machine Intelligence
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Visual Navigation for Mobile Robots: A Survey
Journal of Intelligent and Robotic Systems
M3MIML: A Maximum Margin Method for Multi-instance Multi-label Learning
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Troika - An improved stacking schema for classification tasks
Information Sciences: an International Journal
Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts
Information Sciences: an International Journal
Drosophila gene expression pattern annotation through multi-instance multi-label learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
G3P-MI: A genetic programming algorithm for multiple instance learning
Information Sciences: an International Journal
Robot steering with spectral image information
IEEE Transactions on Robotics
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Classification problems have been frequently encountered in visual mobile robot navigation. The studies reported so far are mainly focused on the single label problem; i.e., each sample (datum) is assigned to a single class. In the case when a sample belongs to multiple classes simultaneously, most existing approaches attempt to avoid handling this situation by labeling the samples subjectively with the base class, which is the most obvious to them, or by considering them as a new class. In this paper, a new multi-instance multi-label learning (MIML) algorithm, called MIMLGP, is proposed by using Gaussian process (GP) for solving the multiple labels problems in visual mobile robot navigation. Compared with the existing multi-label (ML) algorithms, the MIMLGP method represents each sample with multiple instances so that higher accuracy may be achieved. Moreover, correlations between the labels associated to the same sample, which are crucial to multi-label learning but rarely considered before, are analyzed by using a covariance matrix present in MIMLGP. Experiments in the area of place recognition and terrain classification have been conducted to substantiate the proposed algorithm. The experimental results show that the proposed algorithm can achieve better performance than the one produced by the existing algorithms.