csMTL, or context-sensitive Multiple Task Learning, is presented as a method of inductive transfer that uses a single output neural network and additional contextual inputs for learning multiple tasks. The csMTL approach is demonstrated to produce hypotheses that are equivalent to or better than standard MTL hypotheses when learning a primary task in the presence of related and unrelated tasks....

Data mining is the process of automatic extraction of novel, useful and understandable patterns in very large databases. High-performance, scalable, and parallel computing algorithms are crucial in data mining as datasets grow in size and complexity. Inductive logic is a research area in the intersection of machine learning and logic programming, which has been recently applied to data mining. ...

A self-organizing incremental learning model that attempts to combine inductive learning with prior knowledge and default reasoning is described. The inductive learning scheme accounts for useful generalizations and dynamic priority allocation, and effectively supplements prior knowledge. New rules may be created and existing rules modified, thus allowing the system to evolve over time. By comb...

Due to the vast and rapid increase in data, data mining has become an increasingly important tool for the purpose of knowledge discovery in order to prevent the presence of rich data but poor knowledge. Data mining tasks can be undertaken in two ways, namely, manual walkthrough of data and use of machine learning approaches. Due to the presence of big data, machine learning has thus become a po...

We propose an approach for the integration of abduction and induction in the context of Logic Programming. The integration is obtained by extending an Inductive Logic Programming system with abductive reasoning capabilities. In the resulting system, abduction is used to make assumptions in order to cover positive examples and avoid the coverage of negative ones. The assumptions generated can th...

Most recent research of scalable inductive learning on very large dataset, decision tree construction in particular, focuses on eliminating memory constraints and reducing the number of sequential data scans. However, state-of-the-art decision tree construction algorithms still require multiple scans over the data set and use sophisticated control mechanisms and data structures. We first discus...

Both explanation-based and inductive learning techniques have proven successful in a variety of distributed domains. However, learning in multi-agent systems does not necessarily involve the participation of other agents directly in the inductive process itself. Instead, many systems frequently employ multiple instances of induction separately, or singleagent learning. In this paper we present ...

Multiple task learning (MTL) neural networks are one of the better documented methods of inductive transfer of task knowledge (Caruana 1997). An MTL network is a feedforward multi-layer network with an output node for each task being learned. The standard back-propagation of error learning algorithm is used to train all tasks in parallel. The sharing of internal representation in the hidden nod...

We describe a new approach for learning procedural knowledge represented as Teleoreactive Logic Programs (TLPs) using relational behavior traces as input. A TLP organizes task decomposition skills hierarchically and attaches them explicitly defined goals. Our approach integrates analytical learning with inductive generalization in order to learn these skills. The analytical component predicts t...