H aving the "last word" in a company of this experience and distinction would be quite intimidating, were it indeed the end of something but the first and most important thing I have to say is that it must not be the last word! I hope this is the beginning of sustained dialogue and fruitful discussions between and among many of us who have newly or freshly recognized our common resolve in maint...

The training of a support vector machine (SVM) has a time complexity between O(n) and O(n). Most training algorithms for SVM are not suitable for large data sets. Decision trees can simplify SVM training, however the classification accuracy becomes lower when there are inseparable points. This paper introduces a novel method for SVM classification. A decision tree is used to detect low entropy ...

Non-linear mapping functions have long been used in SVM to transform data into a higher dimensional space, allowing the classifier to separate linearly inseparable data. Kernel tricks are used to handle the huge number of features of the mapped data point. However, the training/testing for large data is often time consuming. Following the recent advances in training large linear SVM (i.e., SVM ...

We show how the principle of sparse coding may be applied to learn the forms of structure occurring in time-varying natural images. A sequence of images is described as a linear superposition of space-time functions , each of which is convolved with a time-varying coeecient signal. When a sparse, independent representation is sought over the coeecients, the basis functions that emerge are space...