Knowledge-based artificial neural networks
Artificial Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
The Relaxed Online Maximum Margin Algorithm
Machine Learning
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Sparse Online Learning via Truncated Gradient
The Journal of Machine Learning Research
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Learning with boundary conditions
Neural Computation
Self-advising support vector machine
Knowledge-Based Systems
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Prior knowledge, in the form of simple advice rules, can greatly speed up convergence in learning algorithms. Online learning methods predict the label of the current point and then receive the correct label (and learn from that information). The goal of this work is to update the hypothesis taking into account not just the label feedback, but also the prior knowledge, in the form of soft polyhedral advice, so as to make increasingly accurate predictions on subsequent examples. Advice helps speed up and bias learning so that generalization can be obtained with less data. Our passive-aggressive approach updates the hypothesis using a hybrid loss that takes into account the margins of both the hypothesis and the advice on the current point. Encouraging computational results and loss bounds are provided.