International Journal on Artificial …, Jan 1, 2009
This research presents a new learning model, the Parallel Decision DAG (PDDAG), and shows how to ... more This research presents a new learning model, the Parallel Decision DAG (PDDAG), and shows how to use it to represent an ensemble of decision trees while using significantly less storage. Ensembles such as Bagging and Boosting have a high probability of encoding redundant data structures, and PDDAGs provide a way to remove this redundancy in decision tree based ensembles. When trained by encoding an ensemble, the new model behaves similar to the original ensemble, and can be made to perform identically to it. The reduced storage requirements allow an ensemble approach to be used in cases where storage requirements would normally be exceeded, and the smaller model can potentially execute faster by reducing redundant computation.
In theory, learning is not possible over all tasks in general. In practice, the tasks for which l... more In theory, learning is not possible over all tasks in general. In practice, the tasks for which learning is desired exhibit significant regularity, which makes learning practical. For the most effective learning, it is valuable to understand the nature of this regularity and how it manifests in the tasks where learning is applied. This research presents the DICES distance metric for finding similarity between learning tasks. With this distance metric, a collection of learning tasks can be given a distance matrix. This distance matrix can be used for visualizing the relationships between learning tasks and searching through task space for tasks which are similar in nature. Examples of task visualization are given, and other possible applications of this tool are touched upon. Such applications include learning algorithm selection, transfer learning, and analysis of empirical results.
Many learning algorithms have been developed to solve various problems. Machine learning practiti... more Many learning algorithms have been developed to solve various problems. Machine learning practitioners must use their knowledge of the merits of the algorithms they know to decide which to use for each task. This process often raises questions such as: (1) If performance is poor after trying certain algorithms, which should be tried next? (2) Are some learning algorithms the same in terms of actual task classification? (3) Which algorithms are most different from each other? (4) How different? (5) Which algorithms should be tried for a particular problem? This research uses the COD (Classifier Output Difference) distance metric for measuring how similar or different learning algorithms are. The COD quantifies the difference in output behavior between pairs of learning algorithms. We construct a distance matrix from the individual COD values, and use the matrix to show the spectrum of differences among families of learning algorithms. Results show that individual algorithms tend to cluster along family and functional lines. Our focus, however, is on the structure of relationships among algorithm families in the space of algorithms, rather than on individual algorithms. A number of visualizations illustrate these results. The uniform numerical representation of COD data lends itself to human visualization techniques.
Often the best artificial neural network to solve a real world problem is relatively complex. How... more Often the best artificial neural network to solve a real world problem is relatively complex. However, with the growing popularity of smaller computing devices (handheld computers, cellular telephones, automobile interfaces, etc.), there is a need for simpler models with comparable accuracy. The following research presents evidence that using a larger model as an oracle to train a smaller model on unlabeled data results in 1) a simpler acceptable model and 2) improved results over standard training methods on a similarly sized smaller model. On automated spoken digit recognition, oracle learning resulted in an artificial neural network of half the size that 1) maintained comparable accuracy to the larger neural network, and 2) obtained up to a 25% decrease in error over standard training methods.
Cooperative organic mine avoidance path planning. [Proceedings of SPIE 5794, 1310 (2005)]. Christ... more Cooperative organic mine avoidance path planning. [Proceedings of SPIE 5794, 1310 (2005)]. Christopher B. McCubbin, Christine D. Piatko, Adam V. Peterson, Creighton R. Donnald, David Cohen. Abstract. The JHU/APL Path ...
AbstractOften the best artificial neural network to solve a real world problem is relatively com... more AbstractOften the best artificial neural network to solve a real world problem is relatively complex. However, with the growing popularity of smaller computing devices (handheld computers, cellular telephones, automobile interfaces, etc.), there is a need for simpler ...
This research describes the COD (Classifier Output Difference) distance metric for finding simila... more This research describes the COD (Classifier Output Difference) distance metric for finding similarity between hypotheses and learning algorithms. This metric is a tool which can be used to measure how similar two hypotheses are. It goes beyond simple accuracy comparisons and provides insights about fundamental differences between learning models. This paper describes how COD works and shows how it can be used to predict the potential for combining hypotheses in an ensemble to improve accuracy.
Electronic Components and Technology …, Jan 1, 1990
Page 1. Silicones with Improved Thermal Conductivity for Thermal Management in Electronic Packagi... more Page 1. Silicones with Improved Thermal Conductivity for Thermal Management in Electronic Packaging Adam L. Peterson Speciality Electronic Materials Development Dow Corning Corporation 3901 S . Saginaw Road Midland, Michigan. ...
International Journal on Artificial …, Jan 1, 2009
This research presents a new learning model, the Parallel Decision DAG (PDDAG), and shows how to ... more This research presents a new learning model, the Parallel Decision DAG (PDDAG), and shows how to use it to represent an ensemble of decision trees while using significantly less storage. Ensembles such as Bagging and Boosting have a high probability of encoding redundant data structures, and PDDAGs provide a way to remove this redundancy in decision tree based ensembles. When trained by encoding an ensemble, the new model behaves similar to the original ensemble, and can be made to perform identically to it. The reduced storage requirements allow an ensemble approach to be used in cases where storage requirements would normally be exceeded, and the smaller model can potentially execute faster by reducing redundant computation.
In theory, learning is not possible over all tasks in general. In practice, the tasks for which l... more In theory, learning is not possible over all tasks in general. In practice, the tasks for which learning is desired exhibit significant regularity, which makes learning practical. For the most effective learning, it is valuable to understand the nature of this regularity and how it manifests in the tasks where learning is applied. This research presents the DICES distance metric for finding similarity between learning tasks. With this distance metric, a collection of learning tasks can be given a distance matrix. This distance matrix can be used for visualizing the relationships between learning tasks and searching through task space for tasks which are similar in nature. Examples of task visualization are given, and other possible applications of this tool are touched upon. Such applications include learning algorithm selection, transfer learning, and analysis of empirical results.
Many learning algorithms have been developed to solve various problems. Machine learning practiti... more Many learning algorithms have been developed to solve various problems. Machine learning practitioners must use their knowledge of the merits of the algorithms they know to decide which to use for each task. This process often raises questions such as: (1) If performance is poor after trying certain algorithms, which should be tried next? (2) Are some learning algorithms the same in terms of actual task classification? (3) Which algorithms are most different from each other? (4) How different? (5) Which algorithms should be tried for a particular problem? This research uses the COD (Classifier Output Difference) distance metric for measuring how similar or different learning algorithms are. The COD quantifies the difference in output behavior between pairs of learning algorithms. We construct a distance matrix from the individual COD values, and use the matrix to show the spectrum of differences among families of learning algorithms. Results show that individual algorithms tend to cluster along family and functional lines. Our focus, however, is on the structure of relationships among algorithm families in the space of algorithms, rather than on individual algorithms. A number of visualizations illustrate these results. The uniform numerical representation of COD data lends itself to human visualization techniques.
Often the best artificial neural network to solve a real world problem is relatively complex. How... more Often the best artificial neural network to solve a real world problem is relatively complex. However, with the growing popularity of smaller computing devices (handheld computers, cellular telephones, automobile interfaces, etc.), there is a need for simpler models with comparable accuracy. The following research presents evidence that using a larger model as an oracle to train a smaller model on unlabeled data results in 1) a simpler acceptable model and 2) improved results over standard training methods on a similarly sized smaller model. On automated spoken digit recognition, oracle learning resulted in an artificial neural network of half the size that 1) maintained comparable accuracy to the larger neural network, and 2) obtained up to a 25% decrease in error over standard training methods.
Cooperative organic mine avoidance path planning. [Proceedings of SPIE 5794, 1310 (2005)]. Christ... more Cooperative organic mine avoidance path planning. [Proceedings of SPIE 5794, 1310 (2005)]. Christopher B. McCubbin, Christine D. Piatko, Adam V. Peterson, Creighton R. Donnald, David Cohen. Abstract. The JHU/APL Path ...
AbstractOften the best artificial neural network to solve a real world problem is relatively com... more AbstractOften the best artificial neural network to solve a real world problem is relatively complex. However, with the growing popularity of smaller computing devices (handheld computers, cellular telephones, automobile interfaces, etc.), there is a need for simpler ...
This research describes the COD (Classifier Output Difference) distance metric for finding simila... more This research describes the COD (Classifier Output Difference) distance metric for finding similarity between hypotheses and learning algorithms. This metric is a tool which can be used to measure how similar two hypotheses are. It goes beyond simple accuracy comparisons and provides insights about fundamental differences between learning models. This paper describes how COD works and shows how it can be used to predict the potential for combining hypotheses in an ensemble to improve accuracy.
Electronic Components and Technology …, Jan 1, 1990
Page 1. Silicones with Improved Thermal Conductivity for Thermal Management in Electronic Packagi... more Page 1. Silicones with Improved Thermal Conductivity for Thermal Management in Electronic Packaging Adam L. Peterson Speciality Electronic Materials Development Dow Corning Corporation 3901 S . Saginaw Road Midland, Michigan. ...
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