Frequent itemset mining is a classic problem in data mining. It is a non-supervised process which concerns in finding frequent patterns (or itemsets) hidden in large volumes of d...
Adriano Veloso, Wagner Meira Jr., Srinivasan Parth...
Information integration is often faced with the problem that different data sources represent the same set of the real-world objects, but give conflicting values for specific prop...
Fine-grained categorization refers to the task of classifying objects that belong to the same basic-level class (e.g. different bird species) and share similar shape or visual app...
Abstract— Meta-Learning has been used to predict the performance of learning algorithms based on descriptive features of the learning problems. Each training example in this cont...
The paper focuses on evaluating constraint satisfaction search algorithms on application based random problem instances. The application we use is a well-studied problem in the el...