-
Article
Open AccessComparison of novelty detection methods for multispectral images in rover-based planetary exploration missions
Science teams for rover-based planetary exploration missions like the Mars Science Laboratory Curiosity rover have limited time for analyzing new data before making decisions about follow-up observations. Ther...
-
Article
Visualizing image content to explain novel image discovery
The initial analysis of any large data set can be divided into two phases: (1) the identification of common trends or patterns and (2) the identification of anomalies or outliers that deviate from those trends...
-
Reference Work Entry In depth
Constrained Clustering
-
Article
Machine learning for science and society
The special issue on “Machine Learning for Science and Society” showcases machine learning work with influence on our current and future society. These papers address several key problems such as how we perfor...
-
Article
Machine learning in space: extending our reach
We introduce the challenge of using machine learning effectively in space applications and motivate the domain for future researchers. Machine learning can be used to enable greater autonomy to improve the dur...
-
Reference Work Entry In depth
Constrained Clustering
-
Article
Progressive refinement for support vector machines
Support vector machines (SVMs) have good accuracy and generalization properties, but they tend to be slow to classify new examples. In contrast to previous work that aims to reduce the time required to fully c...
-
Chapter and Conference Paper
Value, Cost, and Sharing: Open Issues in Constrained Clustering
Clustering is an important tool for data mining, since it can identify major patterns or trends without any supervision (labeled data). Over the past five years, semi-supervised (constrained) clustering method...
-
Chapter and Conference Paper
Active Learning with Irrelevant Examples
Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled items that are irrele...
-
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...
-
Chapter and Conference Paper
Active Constrained Clustering by Examining Spectral Eigenvectors
This work focuses on the active selection of pairwise constraints for spectral clustering. We develop and analyze a technique for Active Constrained Clustering by Examining Spectral eigenvectorS (ACCESS) deriv...