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Context-Aware MIML Instance Annotation: Exploiting Label Correlations With ClassIfier Chains

Creator:

Briggs, Forrest, Fern, Xiaoli Z., and Raich, Raviv

Abstract:

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. The MIML formulation can be applied in many domains. For example,...

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Correlations With Classifier Chains Forrest Briggs, Xiaoli Z. Fern, and Raviv Raich School of Electrical
Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach

Creator:

Briggs, Forrest, Lakshminarayanan, Balaji, Neal, Lawrence, Fern, Xiaoli Z., Raich, Raviv, Hadley, Sarah J. K., Hadley, Adam S., and Betts, Matthew G.

Abstract:

Although field-collected recordings typically contain multiple simultaneously vocalizing birds of different species, acoustic species classification in this setting has received little study so far. This work formulates the problem of classifying the set of species present in an audio recording using the multi-instance multi-label (MIML) framework for machine learning, and...

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species: A multi-instance multi-label approach Forrest Briggs,a) Balaji Lakshminarayanan, Lawrence Neal