112,045 research outputs found
Data for: On The Radon--Nikodym Spectral Approach With Optimal Clustering.
No full textThis is the code to accompany the paper "On The Radon--Nikodym Spectral Approach With Optimal Clustering". This is a software implementing the algorithms of interpolation, classification, and optimal clustering based on the Lebesgue quadrature technique. Whereas in a Bayesian approach new observations change only outcome probabilities, in the Radon-Nikodym approach not only outcome probabilities but also the probability space change with new observations. This is a remarkable feature of the approach: both the probabilities and the probability space are constructed from the data. A regular PCA variation expansion depends on attributes normalizing. The PCA variation expansion in the Lebesgue quadrature basis is unique thus does not depend on attributes scale, moreover it is invariant relatively any non-degenerated linear transform of input vector components
Data for: On The Radon--Nikodym Spectral Approach With Optimal Clustering.
No full textThis is the code to accompany the paper "On The Radon--Nikodym Spectral Approach With Optimal Clustering". This is a software implementing algorithms of interpolation, classification, and optimal clustering. Whereas in a Bayesian approach new observations change only outcome probabilities in the Radon-Nikodym approach not only outcome probabilities but also the probability space change with new observations. This is a remarkable feature of the approach: both the probabilities and the probability space are constructed from the data
Data for: Market Dynamics: On Directional Information Derived from (Time, Execution Price, Shares Traded) Transaction Sequences
No full textThis is the software to accompany the paper "Market Dynamics: On Directional Information Derived from (Time, Execution Price, Shares Traded) Transaction Sequences
Data for: On The Radon--Nikodym Spectral Approach With Optimal Clustering.
No full textThis is the code to accompany the paper "On The Radon--Nikodym Spectral Approach With Optimal Clustering". This is a software implementing the algorithms of interpolation, classification, and optimal clustering based on the Lebesgue quadrature technique. Whereas in a Bayesian approach new observations change only outcome probabilities, in the Radon-Nikodym approach not only outcome probabilities but also the probability space change with new observations. This is a remarkable feature of the approach: both the probabilities and the probability space are constructed from the data. A regular PCA variation expansion depends on attributes normalizing. The PCA variation expansion in the Lebesgue quadrature basis is unique thus does not depend on attributes scale, moreover it is invariant relatively any non-degenerated linear transform of input vector components
Data for: Market Dynamics: On Directional Information Derived from (Time, Execution Price, Shares Traded) Transaction Sequences
No full textThis is the software to accompany the paper "Market Dynamics: On Directional Information Derived from (Time, Execution Price, Shares Traded) Transaction Sequences
Data for: On The Radon--Nikodym Spectral Approach With Optimal Clustering.
No full textThis is the code to accompany the paper "On The Radon--Nikodym Spectral Approach With Optimal Clustering". This is a software implementing the algorithms of interpolation, classification, and optimal clustering based on the Lebesgue quadrature technique. Whereas in a Bayesian approach new observations change only outcome probabilities, in the Radon-Nikodym approach not only outcome probabilities but also the probability space change with new observations. This is a remarkable feature of the approach: both the probabilities and the probability space are constructed from the data. A regular PCA variation expansion depends on attributes normalizing. The PCA variation expansion in the Lebesgue quadrature basis is unique thus does not depend on attributes scale, moreover it is invariant relatively any non-degenerated linear transform of input vector components
Data for: Market Dynamics: On Directional Information Derived from (Time, Execution Price, Shares Traded) Transaction Sequences
No full textThis is the software to accompany the paper "Market Dynamics: On Directional Information Derived from (Time, Execution Price, Shares Traded) Transaction Sequences
Data for: On The Radon--Nikodym Spectral Approach With Optimal Clustering.
No full textThis is the code to accompany the paper "On The Radon--Nikodym Spectral Approach With Optimal Clustering". This is a software implementing the algorithms of interpolation, classification, and optimal clustering based on the Lebesgue quadrature technique. Whereas in a Bayesian approach new observations change only outcome probabilities, in the Radon-Nikodym approach not only outcome probabilities but also the probability space change with new observations. This is a remarkable feature of the approach: both the probabilities and the probability space are constructed from the data. A regular PCA variation expansion depends on attributes normalizing. The PCA variation expansion in the Lebesgue quadrature basis is unique thus does not depend on attributes scale, moreover it is invariant relatively any non-degenerated linear transform of input vector components
Data for: 3361478
No full textThis is the software to accompany the paper "Market Dynamics: On Directional Information Derived From (Time, Execution Price, Shares Traded) Transaction Sequences
Data for: On The Radon--Nikodym Spectral Approach With Optimal Clustering.
No full textThis is the code to accompany the paper "On The Radon--Nikodym Spectral Approach With Optimal Clustering". This is a software implementing the algorithms of interpolation, classification, and optimal clustering based on the Lebesgue quadrature technique. Whereas in a Bayesian approach new observations change only outcome probabilities, in the Radon-Nikodym approach not only outcome probabilities but also the probability space change with new observations. This is a remarkable feature of the approach: both the probabilities and the probability space are constructed from the data. A regular PCA variation expansion depends on attributes normalizing. The PCA variation expansion in the Lebesgue quadrature basis is unique thus does not depend on attributes scale, moreover it is invariant relatively any non-degenerated linear transform of input vector components
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