For classification, support vector machines (SVMs) have recently been introduced and
quickly became the state of the art. Now, the incorporation of prior knowledge into SVMs is …

We propose a one-class neural network (OC-NN) model to detect anomalies in complex
data sets. OC-NN combines the ability of deep networks to extract a progressively rich …

Multiple kernel learning aims at simultaneously learning a kernel and the associated
predictor in supervised learning settings. For the support vector machine, an efficient and …

Standard SVM training has O (m3) time and O (m2) space complexities, where m is the
training set size. It is thus computationally infeasible on very large data sets. By observing …

Being among the most popular and efficient classification and regression methods currently
available, implementations of support vector machines exist in almost every popular …

Very high dimensional learning systems become theoretically possible when training
examples are abundant. The computing cost then becomes the limiting factor. Any efficient …

In regression, the desired estimate of y| x is not always given by a conditional mean,
although this is most common. Sometimes one wants to obtain a good estimate that satisfies …

Online algorithms that process one example at a time are advantageous when dealing with
very large data or with data streams. Stochastic Gradient Descent (SGD) is such an …

Abstract Support Vector Machines (SVMs) suffer from a widely recognized scalability
problem in both memory use and computational time. To improve scalability, we have …

The increase in spatial and spectral resolution of the satellite sensors, along with the
shortening of the time-revisiting periods, has provided high-quality data for remote sensing …