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2 Learning stable concepts in domains with hidden changes in context - Harries, Horn (1996)(Correct)
This paper presents Splice, a batch metalearning system, designed to learn locally stable concepts in domains with hidden changes in context. The majority of machine learning algorithms assume that ta... / is invalid. For example financial prediction medical diagnosis and br Strategies Unit Australian Gilt Securities Limited Sydney Australia
Learning stable concepts in a changing world - Harries, Horn (1990)(Correct)
Concept drift due to hidden changes in context complicates learning in many domains including financial prediction, medical diagnosis, and network performance. Existing machine learning approaches to ... / in many domains including financial prediction medical diagnosis and br Strategies Unit Australian Gilt Securities Limited Australia
Detecting Concept Drift in Financial Time Series Prediction using.. - Harries, Horn (1995)(Correct)
This paper investigates the use of strategies to enhance an existing machine learning tool, C4.5, to deal with concept drift and non-determinism in a time series domain. Temporal prediction is a diffi... / changing target concepts. Financial prediction is a challenging target br R. R. DeSieno D.Trading Equity Index Futures with a Neural
DRAFT June 2, 1996: Learning stable concepts in domains with hidden.. - Harries, Horn (1996)(Correct)
This paper presents Splice, a batch metalearning system, designed to learn locally stable concepts in domains with hidden changes in context. The majority of machine learning algorithms assume that ta... / is invalid. For example financial prediction medical diagnosis and br Strategies Unit Australian Gilt Securities Limited Australia
The Benefit of Information Reduction for Trading Strategies - Schittenkopf, Tino, Dorffner (2000)(Correct)
Motivated by previous findings that discretization of financial time series can
effectively filter the data and reduce the noise, this experimental study compares
the trading performance of predicti... / is a promising approach to financial prediction tasks undermining the br and S. J. Hong Predicting equity returns from securities data in
Financial Markets: Very Noisy Information Processing - Magdon-Ismail, Nicholson, Abu-Mostafa (1998)(Correct)
INTRODUCTION
Information processing of financial data entails the extraction
of relevant information from overwhelming noise.
The levels of noise in financial markets are such that the
most one can h... / Financial Time Series Prediction Financial Markets Present Us With br noise plays a role as a tradable commodity in its own right. Indeed market