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We are interested in estimating α by trying to minimize the conditional sum of squares Q = ∑ t = 1 T d H 2 ( X t, α X t − 1 ).
The maximum mean discrepancy distance measure is modified to measure the distance between the conditional distributions and is integrated into the PCA optimization algorithm to minimize the conditional distributions.
Taking CSI mismatch of H 1 and H 2 into account, the proposed objective function is to minimize the conditional MSE between the transmitted and received signals subject to relay power constraint, which is given as follows: begin{array}{*{20}l} &min_{mathbf{Q},mathbf{W}} Eleft[parallelmathbf{widehat{s}}-mathbf{s}{parallel_{2}^{2}}|widehat{mathbf{H}_{1}},widehat{mathbf{H}_{2}}right], end{array} (9a).
Similar to MCG, the H matrix could be used to minimize the conditional variances i.e. the MCH method.
If we are interested in classification of cancer based on a gene pair, then we wish to select the two genes that minimize the conditional entropy H(C| G1, G2), or, equivalently, maximize the mutual information I(G1, G2; C).
This will minimize the conditional formatting box.
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We are interested in the inference of network topology from temporal expression profiles by minimizing the conditional entropy between the genes, i.e., the gene entropy conditioned to the state of others genes.
We design an importance sampling scheme for backward stochastic differential equations (BSDEs) that minimizes the conditional variance occurring in least-squares Monte-Carlo (LSMC) algorithms.
The model is trained discriminatively, minimizing the conditional negative log-likelihood of labels over the empirical distribution of the training data: (3) subject to the regularization constraint ‖ λ‖1≤β.
Thus, the method consists in finding the gene v k that maximizes Equation 1 for a given target gene v i, which is equivalent to find the gene v k that minimizes the conditional entropy S v i(t + 1)| v k(t)).
To minimize the average error of the conditional spectral probabilities reported by TD-GF, the best value of log(K) is the average of the log ratio.
More suggestions(15)
minimize the requirements
minimizing the conditional
minimize the human
minimize the bad
minimize the physical
minimize the weighted
minimize the integrated
minimize the multiple
minimize the deleterious
minimize the economic
minimize the statistical
minimize the cumulative
minimize the final
minimize the actual
minimize the first
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