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Filtering by: Advisor Fern, Xiaoli Z. Committee Member Fern, Alan

Bayesian optimization with empirical constraints

Creator:

Azimi, Javad

Abstract:

Bayesian Optimization (BO) methods are often used to optimize an unknown function f(•) that is costly to evaluate. They typically work in an iterative manner. In each iteration, given a set of observation points, BO algorithms select k ≥ 1 points to be evaluated. The results of those points are...

Resource Type:

Dissertation

Full Text:

: Bayesian Optimization with Empirical Constraints Abstract approved: Xiaoli Z. Fern Bayesian Optimization
Open Set Learning with Counterfactual Images

Creator:

Neal, Lawrence W.

Abstract:

In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training. To detect unknown classes while still generalizing to new instances of existing classes, this thesis introduces a dataset augmentation technique called counterfactual image generation. This approach, based on...

Resource Type:

Masters Thesis

Full Text:

Counterfactual Images Abstract approved: Fuxin Li Xiaoli Fern In open set recognition, a classifier must label
Learning multiple non-redundant codebooks with word clustering for document classification

Creator:

Surve, Akshat Sudhakar

Abstract:

The problem of document classification has been widely studied in machine learning and data mining. In document classification, most of the popular algorithms are based on the bag-of-words representation. Due to the high dimensionality of the bag-of-words representation, significant research has been conducted to reduce the dimensionality via different approaches....

Resource Type:

Masters Thesis

Full Text:

: _____________________________________________________________________ Xiaoli Fern The problem of document classification has been widely studied in machine