8 Result(s)
within
Chapter
Ian Davidson
Information Storage and Retrieval
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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...
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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...
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Chapter and Conference Paper
When Efficient Model Averaging Out-Performs Boosting and Bagging
The Bayes optimal classifier (BOC) is an ensemble technique used extensively in the statistics literature. However, compared to other ensemble techniques such as bagging and boosting, BOC is less known and rar...
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Chapter and Conference Paper
Measuring Constraint-Set Utility for Partitional Clustering Algorithms
Clustering with constraints is an active area of machine learning and data mining research. Previous empirical work has convincingly shown that adding constraints to clustering improves performance, with respe...
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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...
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Chapter and Conference Paper
Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results
We explore the use of instance and cluster-level constraints with agglomerative hierarchical clustering. Though previous work has illustrated the benefits of using constraints for non-hierarchical clustering, ...
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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...
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Chapter and Conference Paper
Further Applications of a Particle Visualization Framework
Previous work introduced a 3D particle visualization framework that viewed each data point as a particle affected by gravitational forces. We showed the use of this tool for visualizing cluster results and ano...
