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ÐThe primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory(More)
Classical feature extraction and data projection methods have been well studied in the pattern recognition and exploratory data analysis literature. We propose a number of networks and learning algorithms which provide new or alternative tools for feature extraction and data projection. These networks include a network (SAMANN) for J.W. Sammon's (1969)(More)
Numerous eeorts have been made in developing \intelligent" programs based on the Von Neumann's centralized architecture. However, these eeorts have not been very successful in building general-purpose intelligent systems. Inspired by biological neural networks, researchers in a number of scientiic disciplines are designing artiicial neural networks (ANNs)(More)
We propose a self-organizing network for hyperellipsoidal clustering (HEC). It consists of two layers. The first employs a number of principal component analysis subnetworks to estimate the hyperellipsoidal shapes of currently formed clusters. The second performs competitive learning using the cluster shape information from the first. The network performs(More)
The dynamic marketplace in online advertising calls for ranking systems that are optimized to consistently promote and capitalize better performing ads. The streaming nature of online data inevitably makes an advertising system choose between maximizing its expected revenue according to its current knowledge in short term (exploitation) and trying to learn(More)
A nonlinear projection method is presented to visualize high-dimensional data as a 2D image. The proposed method is based on the topology preserving mapping algorithm of Kohonen. The topology preserving mapping algorithm is used to train a 2D network structure. Then the interpoint distances in the feature space between the units in the network are(More)
In the recent years support vector machines (SVMs) have been successfully applied to solve a large number of classification problems. Training an SVM, usually posed as a quadratic programming (QP) problem, often becomes a challenging task for the large data sets due to the high memory requirements and slow convergence. We propose to apply boosting to(More)
Learning to rank is a relatively new field of study, aiming to learn a ranking function from a set of training data with relevancy labels. The ranking algorithms are often evaluated using information retrieval measures, such as Normalized Discounted Cumulative Gain (NDCG) [1] and Mean Average Precision (MAP) [2]. Until recently, most learning to rank(More)