Open AccessProceedings Article
Knowledge-Based Support Vector Machine Classifiers
Glenn Fung,Olvi L. Mangasarian,Jude W. Shavlik +2 more
- 01 Jan 2002
- Vol. 15, pp 537-544
TL;DR: Numerical results show improvement in test set accuracy after the incorporation of prior knowledge into ordinary, data-based linear support vector machine classifiers, and one experiment shows that a linear classifier, based solely on prior knowledge, far outperforms the direct application of priorknowledge rules to classify data.
read more
Abstract: Prior knowledge in the form of multiple polyhedral sets, each belonging to one of two categories, is introduced into a reformulation of a linear support vector machine classifier. The resulting formulation leads to a linear program that can be solved efficiently. Real world examples, from DNA sequencing and breast cancer prognosis, demonstrate the effectiveness of the proposed method. Numerical results show improvement in test set accuracy after the incorporation of prior knowledge into ordinary, data-based linear support vector machine classifiers. One experiment also shows that a linear classifier, based solely on prior knowledge, far outperforms the direct application of prior knowledge rules to classify data.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Artificial intelligence in medicine
Pavel Hamet,Johanne Tremblay +1 more
TL;DR: AI in medicine, which is the focus of this review, has two main branches: virtual and physical, and the virtual branch includes informatics approaches from deep learning information management to control of health management systems, and active guidance of physicians in their treatment decisions.
1.7K
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
Laura von Rueden,Sebastian Mayer,Katharina Beckh,Bogdan Georgiev,Sven Giesselbach,Raoul Heese,Birgit Kirsch,Julius Pfrommer,Annika Pick,Rajkumar Ramamurthy,Michal Walczak,Jochen Garcke,Christian Bauckhage,Jannis Schuecker +13 more
TL;DR: A definition and proposed concept for informed machine learning is provided, which illustrates its building blocks and distinguishes it from conventional machine learning, and a taxonomy is introduced that serves as a classification framework forinformed machine learning approaches.
Active preference learning for personalized calendar scheduling assistance
Melinda T. Gervasio,Michael D. Moffitt,Martha E. Pollack,Joseph M. Taylor,Tomás E. Uribe +4 more
- 10 Jan 2005
TL;DR: The experimental results provide evidence of PLIANT's ability to learn user preferences under various conditions and reveal the tradeoffs made by the different active learning selection strategies.
Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
Laith Alzubaidi,Mohammed A. Fadhel,Omran Al-Shamma,Jinglan Zhang,José Santamaría,Ye Duan,Sameer Razzaq Oleiwi +6 more
TL;DR: A deep convolutional neural network (DCNN) model that integrates three ideas including traditional and parallel Convolutional layers and residual connections along with global average pooling is designed that can significantly improve the performance considering a reduced number of images in the same domain of the target dataset.
Semantic-based regularization for learning and inference
TL;DR: A unified approach to learning from constraints is proposed, which integrates the ability of classical machine learning techniques to learn from continuous feature-based representations with the ability to reasoning using higher-level semantic knowledge typical of Statistical Relational Learning.
206
References
•Book
The Nature of Statistical Learning Theory
Vladimir Vapnik
- 01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
46K
Learning internal representations by error propagation
David E. Rumelhart,Geoffrey E. Hinton,Ronald J. Williams +2 more
- 01 Jan 1988
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
•Book
Learning internal representations by error propagation
David E. Rumelhart,Geoffrey E. Hinton,Ronald J. Williams +2 more
- 03 Jan 1986
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
16K