AI News, Machine Learning Proceedings 1990

Machine Learning Proceedings 1990

Machine Learning: Proceedings of the Seventh International Conference (1990) covers the research results from 12 disciplines of machine learning represented at the Seventh International Conference on Machine Learning, held on June 21-23, 1990 at the University of Texas in Austin.

The publication ponders on incremental induction of topologically minimal trees, rational analysis of categorization, search control, utility, and concept induction, graph clustering and model learning by data compression, and an analysis of representation shift in concept learning.

Incremental learning

In computer science, incremental learning is a method of machine learning, in which input data is continuously used to extend the existing model's knowledge i.e.

Examples of incremental algorithms include decisions trees (IDE4,[1] ID5R[2]), decision rules,[3] artificial neural networks (RBF networks,[4] Learn++,[5] Fuzzy ARTMAP,[6] TopoART,[7] and IGNG[8]) or the incremental SVM.[9] The aim of incremental learning is for the learning model to adapt to new data without forgetting its existing knowledge, it does not retrain the model.

Bridging the Gap Between Theory and Practice in Machine Learning

Machine learning has become one of the most exciting research areas in the world, with various applications. However, there exists a noticeable gap between ...

[Коллоквиум]: Rough sets: A tool for qualitative knowledge discovery

Rough set theory (RST) was introduced in the early 1980s by Z. Pawlak (1982) and has become a well researched tool for knowledge discovery. The basic ...

16. Complexity: P, NP, NP-completeness, Reductions

MIT 6.046J Design and Analysis of Algorithms, Spring 2015 View the complete course: Instructor: Erik Demaine In this lecture, ..

7. Counting Sort, Radix Sort, Lower Bounds for Sorting

MIT 6.006 Introduction to Algorithms, Fall 2011 View the complete course: Instructor: Erik Demaine License: Creative Commons ..

3 10 A Priori Algorithm 13 07

Developing Bug-Free Machine Learning Systems Using Formal Mathematics

Noisy data, non-convex objectives, model misspecification, and numerical instability can all cause undesired behaviors in machine learning systems. As a result ...

Lec 5 | MIT 6.046J / 18.410J Introduction to Algorithms (SMA 5503), Fall 2005

Lecture 05: Linear-time Sorting: Lower Bounds, Counting Sort, Radix Sort View the complete course at: License: Creative ..

14. Sorting in Linear Time

MIT 6.851 Advanced Data Structures, Spring 2012 View the complete course: Instructor: Erik Demaine Integer: sorting in linear time ..

Design and Evaluation of Effective, Interactive, and Interpretable Machine Learning

Machine learning is ubiquitous in domains such as criminal justice, credit, lending, and medicine. Traditionally, these models are evaluated based on their ...

Low Latency Displays for Augmented Reality

The primary, long-standing goal for Augmented Reality (AR) is bringing the real and virtual together into a common space. To maintain the illusion that these two ...