Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
On Recognizing and Positioning Curved 3-D Objects from Image Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
The nature of statistical learning theory
The nature of statistical learning theory
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Discriminant Analysis for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization by Vector Space Methods
Optimization by Vector Space Methods
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
The FERET September 1996 Database and Evaluation Procedure
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Discriminant analysis and eigenspace partition tree for face and object recognition from views
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Visual motion based behavior learning using hierarchical discriminant regression
Pattern Recognition Letters
Autonomous mental development in high dimensional context and action spaces
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
MESO: Supporting Online Decision Making in Autonomic Computing Systems
IEEE Transactions on Knowledge and Data Engineering
On developmental mental architectures
Neurocomputing
Online-learning and Attention-based Approach to Obstacle Avoidance Using a Range Finder
Journal of Intelligent and Robotic Systems
Feature Extraction using Unit-linking Pulse Coupled Neural Network and its Applications
Neural Processing Letters
Attention Selection with Self-supervised Competition Neural Network and Its Applications in Robot
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Person recognition using facial video information: A state of the art
Journal of Visual Languages and Computing
A new recognition method for natural images
WSEAS Transactions on Computers
Dual-space linear discriminant analysis for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Fisher subspace tree classifier based on neural networks
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Gabor features based method using HDR (G-HDR) for multiview face recognition
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
A neural network based on biological vision learning and its application on robot
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
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The main motivation of this paper is to propose a new classification and regression method for challenging high-dimensional data. The proposed new technique casts classification problems (class labels as output) and regression problems (numeric values as output) into a unified regression problem. This unified view enables classification problems to use numeric information in the output space that is available for regression problems but are traditionally not readily available for classification problems驴distance metric among clustered class labels for coarse and fine classifications. A doubly clustered subspace-based hierarchical discriminating regression (HDR) method is proposed in this work. The major characteristics include: 1) Clustering is performed in both output space and input space at each internal node, termed 驴doubly clustered.驴 Clustering in the output space provides virtual labels for computing clusters in the input space. 2) Discriminants in the input space are automatically derived from the clusters in the input space. These discriminants span the discriminating subspace at each internal node of the tree. 3) A hierarchical probability distribution model is applied to the resulting discriminating subspace at each internal node. This realizes a coarse-to-fine approximation of probability distribution of the input samples, in the hierarchical discriminating subspaces. No global distribution models are assumed. 4) To relax the per class sample requirement of traditional discriminant analysis techniques, a sample-size dependent negative-log-likelihood (NLL) is introduced. This new technique is designed for automatically dealing with small-sample applications, large-sample applications, and unbalanced-sample applications. 5) The execution of HDR method is fast, due to the empirical logarithmic time complexity of the HDR algorithm. Although the method is applicable to any data, we report the experimental results for three types of data: synthetic data for examining the near-optimal performance, large raw face-image data bases, and traditional databases with manually selected features along with a comparison with some major existing methods, such as CART, C5.0, and OC1.