We present a boosting algorithm for structured prediction with theoretical guarantees for learning accurate ensembles of several structured prediction rules for which no prior knowledge is assumed.

Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodol...

This study investigated the opportunity to learn (OTL) that is available to Grade 12 mathematics learners. Learner workbooks were analysed in terms of time on task, curriculum coverage, curriculum coherence, and cognitive demand. Based on these elements, experienced mathematics teachers judged the opportunity that the learners have to achieve more than 60% for each topic. According to the workb...

Inductors, transmission lines, and Tesla transformers have been modeled with lumped-element equivalent circuits for over a century. In a well-known paper from 1904, Paul Drude predicts that the mutual inductance for an unloaded Tesla transformer should be nonreciprocal. This historical curiosity is mostly forgotten today, perhaps because it appears incorrect. However, Drude's prediction is show...

In this article, a Multi-Objective Memetic Algorithm (MA) for rule learning is proposed. Prediction accuracy and interpretation are two measures that conflict with each other. In this approach, we consider accuracy and interpretation of rules sets. Additionally, individual classifiers face other problems such as huge sizes, high dimensionality and imbalance classes’ distribution data sets. This...

A scientific environmental investment prediction plays a crucial role in controlling pollution and avoiding the blind of management. However effective usually has to fact three challenges about diversiform indicators, insufficient data, reliability models. In present study, new model is proposed using extended belief rule-based system (EBRBS) evidential reasoning (ER) rule, called ensemble EBRB...

Multilevel modeling is an increasingly popular technique for analyzing hierarchical data. This article addresses the problem of predicting a future observable y*j in the jth group of a hierarchical data set. Three prediction rules are considered and several analytical results on the relative performance of these prediction rules are demonstrated. In addition, the prediction rules are assessed b...

We address the challenge in crowdsourcing systems of incentivizing people to contribute to the best of their abilities. We focus on the class of crowdsourcing tasks where contributions are provided in pursuit of a single correct answer. This class includes citizen science efforts that seek input from people with identifying events and states in the world. We introduce a new payment rule, called...

A training set of data has been used to construct a rule for predicting future responses. What is the error rate of this rule? The traditional answer to this question is given by cross-validation. The cross-validation estimate of prediction error is nearly unbiased, but can be highly variable. This article discusses bootstrap estimates of prediction error, which can be thought of as smoothed ve...