The weighted majority algorithm
Information and Computation
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Algorithms for enhancing pattern separability, feature selection and incremental learning with applications to gas-sensing electronic nose systems
Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance Series)
Confidence estimation using the incremental learning algorithm, learn
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
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In this paper we present a particular implementation of the Learn++ algorithm: we investigate the predictability of financial movement direction with Learn++ by forecasting the daily movement direction of the Dow Jones. The Learn++ algorithm is derived from the Adaboost algorithm, which is denominated by sub-sampling. The goal of concept learning, according to the probably approximately correct weak model, is to generate a description of another function, called the hypothesis, which is close to the concept, by using a set of examples. The hypothesis which is derived from weak learning is boosted to provide a better composite hypothesis in generalizing the establishment of the final classification boundary. The framework is implemented using multi-layer Perceptron (MLP) as a weak Learner. First, a weak learning algorithm, which tries to learn a class concept with a single input Perceptron, is established. The Learn++ algorithm is then applied to improve the weak MLP learning capacity and introduces the concept of online incremental learning. The proposed framework is able to adapt as new data are introduced and is able to classify.