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Found 9 papers, 0 papers with code
On Machine Learning Knowledge Representation In The Form Of Partially Unitary Operator. Knowledge Generalizing Operator
Whereas only operator $\mathcal{U}$ projections squared are observable $\left\langle\mathit{OUT}|\mathcal{U}|\mathit{IN}\right\rangle^2$ (probabilities), the fundamental equation is formulated for the operator $\mathcal{U}$ itself.
Market Directional Information Derived From (Time, Execution Price, Shares Traded) Sequence of Transactions. On The Impact From The Future
An attempt to obtain market directional information from non-stationary solution of the dynamic equation: "future price tends to the value maximizing the number of shares traded per unit time" is presented.
On The Radon--Nikodym Spectral Approach With Optimal Clustering
The solution to the classification problem requires prior and posterior probabilities that are obtained using the Lebesgue quadrature[1] technique.
On Numerical Estimation of Joint Probability Distribution from Lebesgue Integral Quadratures
In addition to obtaining two Lebesgue quadratures (for $f$ and $g$) from two eigenproblems, the projections of $f$- and $g$- eigenvectors on each other allow to build a joint distribution estimator, the most general form of which is a density-matrix correlation.
On Lebesgue Integral Quadrature
The Gaussian quadrature, for a given measure, finds optimal values of a function's argument (nodes) and the corresponding weights.
Norm-Free Radon-Nikodym Approach to Machine Learning
The eigenvalues give possible $y^{[i]}$ outcomes and corresponding to them eigenvectors $\psi^{[i]}(\mathbf{x})$ define "Cluster Centers".
Multiple-Instance Learning: Radon-Nikodym Approach to Distribution Regression Problem
For distribution regression problem, where a bag of $x$--observations is mapped to a single $y$ value, a one--step solution is proposed.
Multiple--Instance Learning: Christoffel Function Approach to Distribution Regression Problem
On the first step, to model distribution of observations inside a bag, build Christoffel function for each bag of observations.
Radon-Nikodym approximation in application to image analysis
Given sufficient number of moments pixel information can be completely recovered, for insufficient number of moments only partial information can be recovered and the image reconstruction is, at best, of interpolatory type.
