kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning methods, which shows to be a powerful approach of extracting nonlinear features for...
This paper presents a genetic programming based approach for optimizing the feature extraction step of a handwritten character recognizer. This recognizer uses a simple multilayer ...
A new algorithm is presented which approximates the perceived visual similarity between images. The images are initially transformed into a feature space which captures visual str...
We propose a novel approach for categorizing text documents based on the use of a special kernel. The kernel is an inner product in the feature space generated by all subsequences...
Huma Lodhi, John Shawe-Taylor, Nello Cristianini, ...
We consider the question of predicting nonlinear time series. Kernel Dynamical Modeling (KDM), a new method based on kernels, is proposed as an extension to linear dynamical model...
Principal Component Analysis (PCA) is a basis transformation to diagonalize an estimate of the covariance matrix of input data and, the new coordinates in the Eigenvector basis ar...
3D scatterplots are an extension of the ubiquitous 2D scatterplots that is conceptually simple, but so far proved hard to use in practice. But by combining them with a state-of-th...
The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One ...
Assume that we are trying to build a visual recognizer for a particular class of objects--chairs, for example--using existing induction methods. Assume the assistance of a human t...
With recent advances in sensory and mobile computing technology, enormous amounts of data about moving objects are being collected. One important application with such data is aut...
Xiaolei Li, Jiawei Han, Sangkyum Kim, Hector Gonza...