Machine Learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Neural networks and the financial markets: predicting, combining and portfolio optimisation
Neural networks and the financial markets: predicting, combining and portfolio optimisation
Predicting bonds using the linear relevance vector machine
Neural networks and the financial markets
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
A statistical framework for genomic data fusion
Bioinformatics
An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks
Journal of Management Information Systems
Technical analysis: power tools for active investors
Technical analysis: power tools for active investors
On numerical optimization theory of infinite kernel learning
Journal of Global Optimization
CMARS and GAM & CQP-Modern optimization methods applied to international credit default prediction
Journal of Computational and Applied Mathematics
IEEE Transactions on Neural Networks
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Financially motivated kernels based on EURUSD currency data are constructed from limit order book volumes, commonly used technical analysis methods and canonical market microstructure models--the latter in the form of Fisher kernels. These kernels are used through their incorporation into support vector machines (SVM) to predict the direction of price movement for the currency over multiple time horizons. Multiple kernel learning is used to replicate the signal combination process that trading rules embody when they aggregate multiple sources of financial information. Significant outperformance relative to both the individual SVM and benchmarks is found, along with an indication of which features are the most informative for financial prediction tasks. An average accuracy of 55% is achieved when classifying the direction of price movement into one of three categories for a 200 s predictive time horizon.