in instance-based learning, a training set is given to a classifier for classifying new instances. in practice, not all information in the training set is useful for classifiers. therefore, it is convenient to discard irrelevant instances from the training set. this process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...

In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...

In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...

This paper considers methods to design multiple-output networks based on decision diagrams (DDs). TDM (time-division multiplexing) systems transmit several signals on a single line. These methods reduce: 1) hardware; 2) logic levels; and 3) pins. In the TDM realizations, we consider three types of DDs: shared binary decision digrams (SBDDs), shared multiple-valued decision diagrams (SMDDs), and...

A novel hierarchical approach based on decision diagrams (DD) to modelling digital systems is introduced. Two new contributions are proposed: a new class of structurally synthesized binary DDs for modelling structural aspects of digital circuits, and DDs for high-level modelling of systems. Combination of both types of graphs allows to implement uniform formal approach to lowand high-level diag...

Decision diagrams (DDs) have made their way into Petri net (PN) tools either in the form of new tools (usually designed from scratch to use DDs) or as enhancements to existing tools. This paper describes how an existing and established tool (GreatSPN) has been enhanced through the use of DDs provided by an existing open-source library (Meddly). We benchmark the enhanced tool and discuss lessons...

| This paper classi es di erent decision diagrams (DDs) for discrete functions with respect to the domain and range of represented functions. Relationships among di erent DDs and their relations to spectral transforms are also shown. That provides a uni ed interpretation of DDs, and their further classi cation with respect to the spectral transforms.