Physical Sciences and Mathematics Commons^™

Bias-Corrected Bagging In Active Learning With An Actuarial Application, Yangxuan Xu

Undergraduate Student Research Internships Conference

The variable annuity (VA) is a modern insurance product that offers certain guaranteed protection and tax-deferred treatment. Because of the inherent complexity of guarantees’ payoff, the closed-form solution of fair market values (FMVs) is often not available. Most insurance companies depend on Monte Carlo (MC) simulation to price the FMVs of these products, which is an extremely computational intensive and time-consuming approach. The metamodeling approach can be used to circumvent the heavy computation.

In the modeling stage, the bagged tree method has proved to outperform other parametric approaches. Also, a bias-corrected (BC) bagging model was tried and showed significant improvement …

A New Web Search Engine With Learning Hierarchy, Da Kuang

Electronic Thesis and Dissertation Repository

Most of the existing web search engines (such as Google and Bing) are in the form of keyword-based search. Typically, after the user issues a query with the keywords, the search engine will return a flat list of results. When the query issued by the user is related to a topic, only the keyword matching may not accurately retrieve the whole set of webpages in that topic. On the other hand, there exists another type of search system, particularly in e-Commerce web- sites, where the user can search in the categories of different faceted hierarchies (e.g., product types and price …

Active Learning With Generalized Queries, Jun Du

Electronic Thesis and Dissertation Repository

We study active learning with generalized queries in the thesis.

In contrast to supervised learning, active learning can usually achieve the same predictive accuracy with much fewer labeled training examples, thus significantly reducing the labeling cost. However, previous studies of active learning mostly assume that the learner can only ask specific queries (i.e., require labels for specific examples by providing all feature values). For instance, if the task is to predict osteoarthritis based on a patient data set with 30 features, the previous active learners could only ask the specific queries as: does this patient have osteoarthritis, if ID is …