Learning methods for multi-label classification

Our contribution is four-fold: (1) we propose a model with priors for multi-label classification allowing attractive as well as repulsive weights, (2) we cast the learning of this

In this paper, we propose a simple algorithm for overcoming these problems by editing the existing training dataset, and adapting this edited set with different

The best known transformation method is the binary relevance (BR) approach [21]. This approach trans- forms a multi-label classification problem with Q possible classes into

We proposed here machine learning techniques based on given ontology features to predict correctness or relevance and time for reasoning task by a set of

TAG_2005 TAG_2006 TAG_2007 TAG_agdetection TAG_algorithms TAG_amperometry TAG_analysis TAG_and TAG_annotation TAG_antibody TAG_apob TAG_architecture TAG_article

The main advantage of the proposed approach over other multi-label techniques is the production of a p-value for each possible labelset of a new unseen instance, which can be

According to the user to select an image to generate positive sample packs and anti-packages, using multi-instance learning algorithms to learn, using the image retrieval

As supervised learning algorithms build classifiers based on labeled training examples, several of these approaches aim to reduce the amount of time and effort needed to obtain