In many data mining applications we are given a set of training examples and asked to construct a regression machine or a classifier that has low prediction error or low error rate on new examples. An important issue is speed especially when there are large amounts of data. We show how both classification accuracy and prediction error can be reduced by using boosting techniques to implement com...

In many data mining applications we are given a set of training examples and asked to construct a regression machine or a classifier that has low prediction error or low error rate on new examples, respectively. An important issue is speed especially when there are large amounts of data. We show how both classification and prediction error can be reduced by using boosting techniques to implemen...

A quantitative structure-activity relationship (QSAR) study was conducted for the prediction of inhibitory activity of 1-phenyl[2H]-tetrahydro-triazine-3-one analogues as inhibitors of 5-Lipoxygenase. The inhibitory activities of the 1-phenyl[2H]-tetrahydro-triazine-3-one analogues modeled as a function of molecular structures using chemometrics methods such as multiple linear regression (MLR) ...

For a more accurate reservoir prediction of braided delta front, the method High Resolution Sequence Stratigraphy was adopted in Moxizhuang Oil Field with assistance Comprehensive Prediction Error Filter Analysis. J 1s2 divided into 2 fifth-order base-level cycles, 5 sixth-order turnaround surfaces rise to fall, and time-equal correlation shallow front achieved. Through study microfacies frame,...

Traffic prediction constitutes a hot research topic of network metrology. MultiStep ahead prediction allows to predict more values in the future. Then, the result can be used to act proactively in many prediction applications. In this work, the AutoRegressive Integrated Moving Average (ARIMA) model and the linear minimum mean square error (LMMSE) are used for multiStep predicting. Via experimen...

In order to study the prediction ability of combined model based on BP neural network, the information is gathered to established BP neural network model. The BP neural network model is built between the incidence seniority and the influencing factors and whose prediction performance is compared with the traditional model of multivariate and linear regression. Based on this we can set up linear...

Prediction error--a mismatch between expected and actual outcome--is critical to associative accounts of inferential learning. However, it has proven difficult to explore the effects of prediction error using functional magnetic resonance imaging (fMRI) while excluding the confounding effects of stimulus novelty and incorrect responses. In this event-related fMRI study we used a three-stage exp...

Whilst Human Reliability Analysis (HRA) has been well-accepted and integrated into the safety management process in other industries, the application of such error analysis techniques to the problem of complication and reaction to treatment and the associated risks in healthcare is rare. Though the scarcity of HRA techniques in health-care is likely to be due in some part to the safety culture,...