Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Forrest Briggs"'
Publikováno v:
Knowledge and Information Systems. 43:53-79
In multi-instance multi-label (MIML) instance annotation, the goal is to learn an instance classifier while training on a MIML dataset, which consists of bags of instances paired with label sets; instance labels are not provided in the training data.
Publikováno v:
ACM Transactions on Knowledge Discovery from Data. 7:1-30
Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels. For example, an image can be represented as a bag of segments and associ
Autor:
Melissa E. O'Neill, Forrest Briggs
Publikováno v:
International Journal of Knowledge-based and Intelligent Engineering Systems. 12:47-68
Using a strongly typed functional programming language for genetic programming has many advantages, but evolving functional programs with variables requires complex genetic operators with special cases to avoid creating ill-formed programs. We introd
Publikováno v:
ICDM
In multi-instance multi-label (MIML) instance annotation, the goal is to learn an instance classifier while training on a MIML dataset, which consists of bags of instances paired with label sets, instance labels are not provided in the training data.
Autor:
Ken Larrey, Thi Ngoc Tho Nguyen, Joseph Defretin, Jed Irvine, Tapio Manninen, William Cukierski, Chris Hurlburt, Lawrence Neal, Julien Marzat, Tuomas Virtanen, Adam S. Hadley, Xiaoli Z. Fern, Heikki Huttunen, Konstantinos Eftaxias, Hong-Wei Ng, Sarah Frey Hadley, Zhong Lei, Gabor Fodor, Grigorios Tsoumakas, Maxim Milakov, Aleksandr Diment, Yonghong Huang, David R. Callender, Raviv Raich, Anil Thomas, Pekka Ruusuvuori, Matthew G. Betts, Forrest Briggs
Publikováno v:
MLSP
Birds have been widely used as biological indicators for ecological research. They respond quickly to environmental changes and can be used to infer about other organisms (e.g., insects they feed on). Traditional methods for collecting data about bir
Publikováno v:
MLSP
Novelty detection plays an important role in machine learning and signal processing. This paper studies novelty detection in a new setting where the data object is represented as a bag of instances and associated with multiple class labels, referred
Publikováno v:
MLSP
The Ninth Annual Machine Learning for Signal Processing (MLSP) Data Competition Committee has hosted a bird classification challenge at Kaggle.com (http://www.kaggle.com/c/mlsp-2013-birds). For this year's competition, participants were asked to deve
Publikováno v:
Proceedings of Meetings on Acoustics.
We consider the problem of in-situ species monitoring across large spatial and temporal scales. We are interested in the scenario in which omnidirectional microphones are deployed for round-the-clock in-situ species monitoring. In such a scenario, re
Publikováno v:
KDD
Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels. For example, an image can be represented as a bag of segments and associ
Publikováno v:
SSP
We present regularized multiple density estimation (MDE) using the maximum entropy (MaxEnt) framework for multi-instance datasets. In this approach, bags of instances are represented as distributions using the principle of MaxEnt. We learn basis func