Reflective inductive inference of recursive functions

AbstractIn this paper, we investigate reflective inductive inference of recursive functions. A reflective IIM is a learning machine that is additionally able to assess its own competence.First, we formalize reflective learning from arbitrary, and from canonical, example sequences. Here, we arrive at four different types of reflection: reflection in the limit, optimistic, pessimistic and exact reflection.Then, we compare the learning power of reflective IIMs with each other as well as with the one of standard IIMs for learning in the limit, for consistent learning of three different types, and for finite learning

Kārawān, 1347-09-19, 1968-12-10

The volume and issue of this edition is Kārawān, v. 1, no. 66. The numbers in the title refer to the date of the edition, with the first set of numbers representing the Persian calendar and the second set of numbers representing the date in the Gregorian calendar. Editor: 1968- Abdul Haq Waleh. Title transliterated into English : Caravan. Lumṛay kal̄, Shpeg shpetuma ganạh [vol. 1, no. 66] (Qaws. 19, 1347 [December 10, 1968]).https://digitalcommons.unomaha.edu/karawan/1069/thumbnail.jp

Hypothesis Assessments as Guidance for Incremental and Meta-Learning

. In this paper, a new decision tree learning algorithm (INCDT) is proposed: an incremental one based on hypotheses assessments. INC-DT uses only a fixed amount of instance memory during the whole learning process. It bases its hypotheses exclusively on the last one computed, its assessment, and a fixed number of seen examples. So, it is able to deal with initial knowledge and concept drift. Additionally, the hypothesis assessments can be used directly for model selection and combination. Moreover, they make compentence and quality assessment of the algorithm explicit which supports meta-learning algorithms. First, the necessity of incremental learning algorithms is motivated. Work, both theoretical and practical one, regarding incremental learners is discussed. The algorithm INC-DT is described and evaluated empirically. The influence of certain parameters like the buffer size, the base algorithm and the data noise are discussed. 1 Introduction The investigations in the..