7 Result(s)
within
Chapter
Ian Davidson
Information Systems Applications (incl. Internet)
-
Chapter and Conference Paper
Constrained Clustering via Post-processing
Constrained clustering has received much attention since its inception as the ability to add weak supervision into clustering has many uses. Most existing work is algorithm-specific, limited to simple together...
-
Chapter and Conference Paper
Learning Automated Agents from Historical Game Data via Tensor Decomposition
War games and military war games, in general, are extensively played throughout the world to help train people and see the effects of policies. Currently, these games are played by humans at great expense and ...
-
Chapter and Conference Paper
Complex Interactions in Social and Event Network Analysis
Modern social network analytic techniques, such as centrality analysis, outlier detection, and/or segmentation, are limited in that they typically only identify interactions within the dataset occurring as a f...
-
Chapter and Conference Paper
Keyless Steganography in Spatial Domain Using Energetic Pixels
Steganography is the field of hiding messages in apparently innocuous media (e.g. images). Hiding messages in the pixel intensities of images is a popular approach in spatial domain steganography. However, sin...
-
Chapter and Conference Paper
A Dynamic Adaptive Sampling Algorithm (DASA) for Real World Applications: Finger Print Recognition and Face Recognition
In many real world problems, data mining algorithms have access to massive amounts of data (defense and security). Mining all the available data is prohibitive due to computational (time and memory) constraint...
-
Chapter and Conference Paper
An Information Theoretic Optimal Classifier for Semi-supervised Learning
Model uncertainty refers to the risk associated with basing prediction on only one model. In semi-supervised learning, this uncertainty is greater than in supervised learning (for the same total number of inst...
-
Chapter and Conference Paper
Using background contextual knowledge for documents representation
We describe our approach to document representation that captures contextual dependencies between terms in a corpus and makes use of these dependencies to represent documents. We have tried our representation ...
