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Filtering by: Creator Hertz, Eric Degree Field Computer Science

Hybrid visualization of asymmetric tensor fields : glyphs and hyperstreamlines

Creator:

Palke, Darrel

Abstract:

Asymmetric tensor fields are useful for understanding fluid flow and solid deformation. They present new challenges, however, for traditional tensor field visualization techniques such as hyperstreamline placement and glyph packing. This is because the physical behavior of tensors inside real domains where eigenvalues are real is fundamentally different from the...

Resource Type:

Masters Thesis

Full Text:

-Based Techniques Hyperstreamline-based visualization for symmetric tensor fields is utilized by Hertz
Learning with Partial Supervision for Clustering and Classification

Creator:

Pei, Yuanli

Abstract:

In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data is available for learning; classification is a supervised task, where fully-labeled data are collected for training a classifier. In some scenarios, however, we may not...

Resource Type:

Dissertation
Active learning with committees : an approach to efficient learning in text categorization using linear threshold algorithms

Creator:

Liere, Ray

Abstract:

We developed and investigated machine learning methods that require minimal preprocessing of the input data, use few training examples, run fast, and still obtain high levels of accuracy. Most approaches to designing machine learning programs are based on the supervised learning paradigm – training examples are chosen randomly and given...

Resource Type:

Dissertation