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Articles 1 - 3 of 3
Full-Text Articles in Business
Project Foresight Annual Report, 2019-2020, Paul J. Speaker
Project Foresight Annual Report, 2019-2020, Paul J. Speaker
Faculty & Staff Scholarship
Project FORESIGHT is a business-guided self-evaluation of forensic science laboratories across the globe. The participating laboratories represent local, regional, state, and national agencies. Economics, accounting, finance, and forensic faculty provide assistance, guidance, and analysis. Laboratories participating in Project FORESIGHT have developed standardized definitions for metrics to evaluate work processes, linking financial information to work tasks, and functions. Laboratory managers can then assess resource allocations, efficiencies, and value of services—the mission of Project FORESIGHT is to measure, preserve what works, and change what does not.
The benchmark data for the 2019-2020 performance period includes laboratory submissions for a variety of fiscal …
Research Note: The Frequency Of Third Sector Terms In English-Language Books (Shown In 31 Ngrams), Roger A. Lohmann
Research Note: The Frequency Of Third Sector Terms In English-Language Books (Shown In 31 Ngrams), Roger A. Lohmann
Faculty & Staff Scholarship
This research note shows 31 Ngrams that tell us a great deal about the history of a variety of key terms used in contemporary third sector research and, in a number of cases, pinpoint the earliest published uses of the terms and their proximity to other, similar terms.
Effects Of Training Set Size On Supervised Machine-Learning Land-Cover Classification Of Large-Area High-Resolution Remotely Sensed Data, Christopher A. Ramezan, Timothy A. Warner, Aaron E. Maxwell, Bradley S. Price
Effects Of Training Set Size On Supervised Machine-Learning Land-Cover Classification Of Large-Area High-Resolution Remotely Sensed Data, Christopher A. Ramezan, Timothy A. Warner, Aaron E. Maxwell, Bradley S. Price
Faculty & Staff Scholarship
The size of the training data set is a major determinant of classification accuracy. Neverthe- less, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be typical of many real-world applied projects. This work investigates how variations in training set size, ranging from a large sample size (n = 10,000) to a very small sample size (n = 40), affect the performance of six supervised machine-learning algo- rithms applied to classify large-area high-spatial-resolution (HR) (1–5 m) remotely sensed data within the context of a geographic …