model based on ratios of Michelson contrasts best predicted the subjects' settings, which were both accurate and precise. In another experiment, pairs of stimuli selected from a range with various values of the adjustable layer luminance were presented in a series of forced-choice trials. A

We have developed a highly parallel and accelerated circuit simulator which produces precise results for large scale simulation. We incorporated multithreading in both the model and matrix calculations to achieve not only a factor of 10 acceleration compared to the de facto standard circuit

validity of self-reported height and weight in assessing overweight and obesity status among adolescents using a surveillance system

before, and will attempt breakthroughs not possible today. If the size of the problems solved on such machines scales accordingly, we may face new issues related to precision, accuracy, performance, and programmability. The paper examines some relevant aspects of this problem. I.

without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from tredning data in which the values of individual records have been perturbed. The resulting data records look very different from the original records

Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear

Eric Brill introduced transformation-based learning and showed that it can do part-ofspeech tagging with fairly high accuracy. The same method can be applied at a higher level of textual interpretation for locating chunks in the tagged text, including non-recursive "baseNP" chunks

), a Ø 2 -test (CHI), and term strength (TS). We found IG and CHI most effective in our experiments. Using IG thresholding with a knearest neighbor classifier on the Reuters corpus, removal of up to 98% removal of unique terms actually yielded an improved classification accuracy (measured by average

of twelve feature selection methods (e.g. Information Gain) evaluated on a benchmark of 229 text classification problem instances that were gathered from Reuters, TREC, OHSUMED, etc. The results are analyzed from multiple goal perspectives—accuracy, F-measure, precision, and recall—since each is appropriate

Evaluation of point coordinate transformation accuracy is most frequently presented by the references in a general and approximated way. In assumption that a secondary system point coordinates may practically be considered errorless, the following formulas were given in [5]: