Communications of the ACM
Models of incremental concept formation
Artificial Intelligence
Polynomial-time inference of arbitrary pattern languages
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Learning Automata from Ordered Examples
Machine Learning - Connectionist approaches to language learning
Language learning in dependence on the space of hypotheses
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
An incremental concept formation approach for learning from databases
Theoretical Computer Science - Special issue on formal methods in databases and software engineering
Regular Article: Open problems in “systems that learn”
Proceedings of the 30th IEEE symposium on Foundations of computer science
Language learning from texts: mindchanges, limited memory, and monotonicity
Information and Computation
Incremental learning from positive data
Journal of Computer and System Sciences
Program synthesis in the presence of infinite number of inaccuracies
Journal of Computer and System Sciences
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Theoretical Computer Science - Special issue on algorithmic learning theory
Incremental concept learning for bounded data mining
Information and Computation
Vacillatory and BC learning on noisy data
Theoretical Computer Science - Special issue on algorithmic learning theory
Selecting Examples for Partial Memory Learning
Machine Learning
Incremental Induction of Decision Trees
Machine Learning
Machine Learning
A Guided Tour Across the Boundaries of Learning Recursive Languages
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
Synthesizing Noise-Tolerant Language Learners
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
Formal languages and their relation to automata
Formal languages and their relation to automata
On the data consumption benefits of accepting increased uncertainty
Theoretical Computer Science
Some natural conditions on incremental learning
Information and Computation
IEEE Transactions on Neural Networks
AttributeNets: an incremental learning method for interpretable classification
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Towards a better understanding of incremental learning
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Supervised learning with minimal effort
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
| Hi-index | 5.23 |
This paper provides a systematic study of incremental learning from noise-free and from noisy data. As usual, we distinguish between learning from positive data and learning from positive and negative data, synonymously called learning from text and learning from informant. Our study relies on the notion of noisy data introduced by Stephan.The basic scenario, named iterative learning, is as follows. In every learning stage, an algorithmic learner takes as input one element of an information sequence for some target concept and its previously made hypothesis and outputs a new hypothesis. The sequence of hypotheses has to converge to a hypothesis describing the target concept correctly.We study the following refinements of this basic scenario. Bounded example-memory inference generalizes iterative inference by allowing an iterative learner to additionally store an a priori bounded number of carefully chosen data elements, while feedback learning generalizes it by allowing the iterative learner to additionally ask whether or not a particular data element did already appear in the input data seen so far.For the case of learning from noise-free data, we show that, when both positive and negative data are available, restrictions on the accessibility of the input data do not limit the learning capabilities if and only if the relevant iterative learners are allowed to query the history of the learning process or to store at least one carefully selected data element. This insight nicely contrasts the fact that, in case only positive data are available, restrictions on the accessibility of the input data seriously affect the learning capabilities of all versions of incremental learners.For the case of learning from noisy data, we present characterizations of all kinds of incremental learning in terms being independent from learning theory. The relevant conditions are purely structural ones. Surprisingly, when learning from noisy text and noisy informant is concerned, even iterative learners are exactly as powerful as unconstrained learning devices.