In recent work we showed that models constructed from planner performance data over a large suite of benchmark problems are surprisingly accurate; 91-99% accuracy for success and ...
Mark Roberts, Adele E. Howe, Brandon Wilson, Marie...
We introduce the problem of zero-data learning, where a model must generalize to classes or tasks for which no training data are available and only a description of the classes or...
The conversion and extension of the Incremental ParetoCoevolution Archive algorithm (IPCA) into the domain of Genetic Programming classifier evolution is presented. In order to ac...
We show that excluding outliers from the training data significantly improves kNN classifier, which in this case performs about 10% better than the best know method--Centroid-based...
Abstract. We describe progress in the automatic detection and identification of humans in video, given a minimal number of labelled faces as training data. This is an extremely cha...
Linking or matching databases is becoming increasingly important in many data mining projects, as linked data can contain information that is not available otherwise, or that woul...
We apply a well-known Bayesian probabilistic model to textual information retrieval: the classification of documents based on their relevance to a query. This model was previously...
We present ELEM2, a new method for inducing classification rules from a set of examples. The method employs several new strategies in the induction and classification processes to ...
: Precise sensorimotor mappings between various motor, ensor, and abstract physical spaces are the basis for many robotics tasks. Their cheap construction is a challenge for adapti...
In this paper we present a method for the selection of training instances based on the classification accuracy of a SVM classifier. The instances consist of feature vectors repres...
Miguel Lopes, Fabien Gouyon, Alessandro Koerich, L...