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Clustering is a central unsupervised learning task with a wide variety of applications. Not surprisingly, there exist many clustering algorithms. However, unlike classification ta...
We present a flexible new optimization framework for finding effective, reliable pseudo-relevance feedback models that unifies existing complementary approaches in a principled wa...
This paper focuses on the application of a new ACO-based automatic programming algorithm to the classification task of data mining. This new model, called GBAP algorithm, is based ...
We present a fine-grain dynamic instruction placement algorithm for small L0 scratch-pad memories (spms), whose unit of transfer can be an individual instruction. Our algorithm ca...
In this paper, we propose a framework for predicting the performance of a vision algorithm given the input image or video so as to maximize the algorithm's ability to provide...
We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...
We introduce a novel multi-agent patrolling strategy. By assumption, the swarm of agents performing the task consists of very low capability ant-like agents. The agents have littl...
We present the S-Space Package, an open source framework for developing and evaluating word space algorithms. The package implements well-known word space algorithms, such as LSA,...
Various text mining algorithms require the process of feature selection. High-level semantically rich features, such as figurative language uses, speech errors etc., are very prom...