Making large-scale support vector machine learning practical
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Autonomous Driving Goes Downtown
IEEE Intelligent Systems
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Gradient-Based Optimization of Hyperparameters
Neural Computation
Fuzzy integral-based perceptron for two-class pattern classification problems
Information Sciences: an International Journal
Pedestrian detection and tracking in infrared imagery using shape and appearance
Computer Vision and Image Understanding
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Linear dimensionality reduction using relevance weighted LDA
Pattern Recognition
Optical camera based pedestrian detection in rainy or snowy weather
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Pedestrian detection and tracking with night vision
IEEE Transactions on Intelligent Transportation Systems
A Low-Cost Pedestrian-Detection System With a Single Optical Camera
IEEE Transactions on Intelligent Transportation Systems
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
So near and yet so far: New insight into properties of some well-known classifier paradigms
Information Sciences: an International Journal
A ν-twin support vector machine (ν-TSVM) classifier and its geometric algorithms
Information Sciences: an International Journal
Information Sciences: an International Journal
18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Gender classification from unaligned facial images using support subspaces
Information Sciences: an International Journal
Multi-appliance recognition system with hybrid SVM/GMM classifier in ubiquitous smart home
Information Sciences: an International Journal
Information Sciences: an International Journal
Extending twin support vector machine classifier for multi-category classification problems
Intelligent Data Analysis
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Support vector machine (SVM) has become a dominant classification technique used in pedestrian detection systems. In such systems, classifiers are used to detect pedestrians in some input frames. The performance of a SVM classifier is mainly influenced by two factors: the selected features and the parameters of the kernel function. These two factors are highly related and therefore, it is desirable that the two factors can be analyzed simultaneously, which are usually not the case in the previous work. In this paper, we propose an evolutionary method to simultaneously optimize the feature set and the parameters for the SVM classifier. Specifically, adaptive genetic operators were designed to be suitable for the feature selection and parameter tuning. The proposed method is used to train a SVM classifier for pedestrian detection. Experiments in real city traffic scenes show that the proposed approach leads to higher detection accuracy and shorter detection time.