Pattern Recognition with Linearly Structured Labels Using Recursive Kernel Estimator
نویسندگان
چکیده
We consider pattern recognition problem when classes and their labels are linearly structured (or ordered). We propose the loss function based on the squared differences between the true and the predicted class labels. The optimal Bayes classifier is derived and then estimated by the recursive kernel estimator. Its consistency is established theoretically. Its RBF-like realization of the classifier is also proposed together with a recursive learning algorithm, which is well suited for on-line applications. The proposed approach was tested in real life example involving classification of moving vehicles.
منابع مشابه
Adaptive mixtures: Recursive nonparametric pattern recognition
-We develop a method of performing pattern recognition (discrimination and classification) using a recursive technique derived from mixture models, kernel estimation and stochastic approximation. Unsupervised learning Density estimation Kernel estimator Mixture model Stochastic approximation Recursive estimation
متن کاملMAD Loss in Pattern Recognition and RBF Learning
We consider a multi-class pattern recognition problem with linearly ordered labels and a loss function, which measures absolute deviations of decisions from true classes. In the bayesian setting the optimal decision rule is shown to be the median of a posteriori class probabilities. Then, we propose three approaches to constructing an empirical decision rule, based on a learning sequence. Our s...
متن کاملA qualitative analysis of the resistive grid kernel estimator
Poston, W.L., et al., A qualitative analysis of the resistive grid kernel estimator, Pattern Recognition Letters 15 (1993) 219-225. The ability to estimate a probability density function from random data has applications in discriminant analysis and pattern recognition problems. A resistive grid kernel estimator (RGKE) is described which is suitable for hardware implementation. The one-dimensio...
متن کاملWide coverage natural language processing using kernel methods and neural networks for structured data
Convolution kernels and recursive neural networks are both suitable approaches for supervised learning when the input is a discrete structure like a labeled tree or graph. We compare these techniques in two natural language problems. In both problems, the learning task consists in choosing the best alternative tree in a set of candidates. We report about an empirical evaluation between the two ...
متن کاملFast Gradient Computation for Learning with Tensor Product Kernels and Sparse Training Labels
Supervised learning with pair-input data has recently become one of the most intensively studied topics in pattern recognition literature, and its applications are numerous, including, for example, collaborative filtering, information retrieval, and drug-target interaction prediction. Regularized least-squares (RLS) is a kernel-based learning algorithm that, together with tensor product kernels...
متن کامل