Instance reduction techniques can improve generalization, reduce storage requirements and execution time of instance-based learning algorithms. This paper presents an instance reduction algorithm called Adaptive Threshold-based Instance Selection Algorithm (ATISA). ATISA aims to preserve important instances based on a selection criterion that uses the distance of each instance to its nearest en...

The dependency on the quality of the training data has led to significant work in noise reduction for instance-based learning algorithms. This paper presents an empirical evaluation of current noise reduction techniques, not just from the perspective of their comparative performance, but from the perspective of investigating the types of instances that they focus on for removal. A novel instanc...

where G ∈ L∞(IR, IRn×n) is a matrix valued function with G(x+1) = G(x) for almost every x ∈ IR. Systems of this type appear, for instance, in the theory of vibrations or in hydrodynamics. In the typical case where the function G depends on some set of parameters, one is not really interested in the solution of (1) but in the values of the parameters for which this equation is stable. Let Y : IR...

We propose a framework for discriminative Information Retrieval (IR) atop linguistic features, trained to improve the recall of tasks such as answer candidate passage retrieval, the initial step in text-based Question Answering (QA). We formalize this as an instance of linear feature-based IR (Metzler and Croft, 2007), illustrating how a variety of knowledge discovery tasks are captured under t...

the insulin-like growth factor i receptor (igf-ir) plays an essential role in the establishment and maintenance of transformed phenotype. interference with the igf-ir pathway by antisense causes reversal of the transformed phenotype in many rodent and human tumor cell lines. we stably transfected the pc-3 human prostate cancer cell line with an igf-ir antisense rna expression plasmid. the numbe...