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SVM is an approximate implementation of the "method of structural risk minimization" aiming to attend low probability of generalization error.
It is an approximate implementation to the structure risk minimization (SRM) principle in statistical learning theory (SLT), rather than the empirical risk minimization (ERM) method (Kwok 1999).
In some environments the conditions for (exact or approximate) implementation by mechanisms satisfying the best element property are more restrictive than previously thought.
Conversely, support vector machines (SVMs) refer to kernel-based methods, neural-network-alike models that constitute an approximate implementation of the structural risk minimization principle.
The analytical work described includes the nominal design and its performance, the performance degradation due to modeling errors, and the performance degradation due to approximate implementation of the dynamic compensator.
As an approximate implementation of the PHD filter, the particle PHD filter is composed of three steps (for detailed parameter notation, refer to [1]): At time k>0, let L k and Jk denote the number of surviving particles and birthed particles, respectively.
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Approximate implementations of wavelet transforms in analog hardware can be achieved with dynamic translinear circuits.
Its core algorithm is an extension of a distributed approximate kNN implementation, optimized to operate efficiently in a single distributed session.
To best approximate typical implementation by non-expert users, all programs were run with the default or recommended settings from the same desktop machine equipped with 4 Gb of RAM.
The framework in STEPS offers the potential for future improvements to performance, such as approximate method implementation and parallelization, which will add more power and open up even more applications as larger systems may be simulated.
As a result, we derived a definition for initiation of automated eGFR reporting that provided approximate dates of implementation.
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