- Title
- Mass estimation
- Creator
- Ting, Kaiming; Zhou, Guang; Liu, Fei; Tan, Swee
- Date
- 2013
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/74260
- Identifier
- vital:7232
- Identifier
-
https://doi.org/10.1007/s10994-012-5303-x
- Identifier
- ISSN:1573-0565
- Abstract
- This paper introduces mass estimation—a base modelling mechanism that can be employed to solve various tasks in machine learning. We present the theoretical basis of mass and efficient methods to estimate mass. We show that mass estimation solves problems effectively in tasks such as information retrieval, regression and anomaly detection. The models, which use mass in these three tasks, perform at least as well as and often better than eight state-of-the-art methods in terms of task-specific performance measures. In addition, mass estimation has constant time and space complexities.
- Relation
- Machine Learning Vol. 90, no. 1 (2013), p. 127-160
- Rights
- Copyright Springer
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 1702 Cognitive Science; Mass estimation; Density estimation; Information retrieval; Regression; Anomaly detection
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