Details

Autor(en) / Beteiligte

• Ting, Kai Ming
• Zhou, Guang-Tong
• Liu, Fei Tony
• Tan, Swee Chuan

Titel

Mass estimation

Ist Teil von

• Machine learning, 2013, Vol.90 (1), p.127-160

Ort / Verlag

Boston: Springer US

Erscheinungsjahr

2013

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.