The Hierarchical Hidden Markov Model (HHMM) is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. Even if effective algorithms are ava...
We present a new domain for unsupervised learning: automatically customizing the computer to a specific melodic performer by merely listening to them improvise. We also describe B...
For their usage in the semantic web, valid ontologies are required for a given domain. Here we focus on ontologies represented as concept maps (semantic nets). For one and the sam...
The inherent vagueness and ambiguity of non-monotonic reasoning makes it impossible to formulate detailed specifications to validate KBS performance by using traditional test-case...
As real-time and embedded systems become increasingly large and complex, the traditional strictly static approach to memory management begins to prove untenable. The challenge is ...
Andrew Borg, Andy J. Wellings, Christopher D. Gill...