We will describe recent developments in a system for machine learning that we’ve been working on for some time (Sol 86, Sol 89). It is meant to be a “Scientist’s Assistant” of great power and versatility in many areas of science and mathematics. It differs from other ambitious work in this area in that we are not so much interested in knowledge itself, as we are in how it is acquired how machin...

The classical theory of pattern recognition assumes labeled examples appear according to unknown underlying class conditional probability distributions where the pattern classes are picked randomly in a passive manner according to their a priori probabilities. This paper presents experimental results for an incremental nearest-neighbor learning algorithm which actively selects samples from diff...

This paper presents a supervised machine learning approach to incrementally learn and segment affixes using generic background knowledge. We used Prolog script to split an affix from the Amharic word for further morphological analysis. Amharic, a Semitic language, has very complex inflectional and derivational verb morphology, with many possible prefixes and suffixes which are used to show vari...

We propose a long-term memory design for artificial general intelligence based on Solomonoff’s incremental machine learning methods. We use R5RS Scheme and its standard library with a few omissions as the reference machine. We introduce a Levin Search variant based on stochastic Context Free Grammar together with four synergistic update algorithms that use the same grammar as a guiding probabil...