Compressed Kernel Perceptrons

Kernel machines are a popular class of machine learning algorithms that achieve state of the art accuracies on many real-life classification problems. Kernel perceptrons are among the most popular online kernel machines that are known to achieve high-quality classification despite their simplicity. They are represented by a set of B prototype examples, called support vectors, and their associated weights. To obtain a classification, a new example is compared to the support vectors. Both space to store a prediction model and time to provide a single classification scale as O(B). A problem with kernel perceptrons is that the number of support vectors tends to grow without bounds with the number of training examples on noisy data. To reduce the strain at computational resources, budget kernel perceptrons have been developed by upper bounding the number of support vectors. In this work, we proposed a new budget algorithm that upper bounds the number of bits needed to store kernel perceptr...

• Type: Conference
• Year: 2009
• Where: DCC
• Authors: Slobodan Vucetic, Vladimir Coric, Zhuang Wang
• Keywords: Budget Kernel Perceptrons, Computer Graphics, DCC 2009, Kernel Perceptron Algorithm, Support Vectors