Classifying data from protected statistical datasets

Classifying data from protected statistical datasets

Statistical Disclosure Control (SDC) is an active research area in the recent years. The goal is to transform an original dataset X into a protected one X ′, such that X ′ does not reveal any relation between confidential and (quasi-)identifier attributes and such that X ′ can be used to compute reliable statistical information about X. Many specific protection methods have been proposed and an...

Augmented Statistical Models for Classifying Sequence Data

Bootstrapping statistical parsers from small datasets

We present a practical co-training method for bootstrapping statistical parsers using a small amount of manually parsed training material and a much larger pool of raw sentences. Experimental results show that unlabelled sentences can be used to improve the performance of statistical parsers. In addition, we consider the problem of bootstrapping parsers when the manually parsed training materia...

Arguing from Experience to Classifying Noisy Data

A process, based on argumentation theory, is described for classifying very noisy data. More specifically a process founded on a concept called “arguing from experience” is described where by several software agents “argue” about the classification of a new example given individual “case bases” containing previously classified examples. Two “arguing from experience” protocols are described: PAD...

Classifying Instantaneous Cognitive States from fMRI Data

We consider the problem of detecting the instantaneous cognitive state of a human subject based on their observed functional Magnetic Resonance Imaging (fMRI) data. Whereas fMRI has been widely used to determine average activation in different brain regions, our problem of automatically decoding instantaneous cognitive states has received little attention. This problem is relevant to diagnosing...