Machine algorithm

Doniger S, Hofmann T, Yeh J. Predicting CNS permeability of drug molecules comparison of neural network and support vector machine algorithms. / Comp Biol 2002 9 849-64. 

Finally we remark that the majority of the parameters in the STCA filters have direct physical or mechanical interpretation, and that the transparency of the classification process is an important component in assuring the safety case for STCA. However, whether tuned by hand or optimised by a machine algorithm, the operational parameters are inferred from data. An alternative to direct physical modelling is to employ purely statistical classifiers, for example Ic-nearest neighbour classifiers or neural networks, for which there is no ready interpretation of the parameters. Nonetheless, these methods are highly effective in other areas and the machine optimisation of STCA parameters blurs the distinction between physical models on one hand and statistical black boxes on the other. We look forward to the construction of safety cases for purely statistical classifiers whose operational parameters are inferred from data and which have no ready physical interpretation. 

Our multipole code D-PMTA, the Distributed Parallel Multipole Tree Algorithm, is a message passing code which runs both on workstation clusters and on tightly coupled machines such as the Cray T3D/T3E [11]. Figure 3 shows the parallel performance of D-PMTA on a moderately large simulation on the Cray T3E the scalability is not affected by adding the macroscopic option. 

D.E. Goldberg, Genetic Algorithms in Search, Optimi2ation and Machine Learning, Addison-Wesley, New York, 1989. 

The development of efficient algorithms and the sophisticated description of long-range electrostatic effects allow calculations on systems with 100 000 atoms and more, which address biochemical problems like membrane-bound protein complexes or the action of molecular machines . 

In recent decades, computer scientists have tried to provide computers with the ability to learn. This area of research was summarized under the umbrella term "machine learning . Today machine learning is defined as "the study of computer algorithms that improve automatically through experience [1]. 

Cooley J W and ] W Tukey 1965. An Algorithm for the Machine Calculation of Complex Fourier Series Aiathemalics of Computation 19 297-301. 

Goldberg D E 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA., Addison-W esley. 

The special design of the Latham bowl allows for a specific blood cell separation known as SURGE. This technique makes use of the principle of critical velocity. The Latham bowl is filled until the huffy coat, ie, layer of platelets and white cells, moves in front of the bowl optics. At this point the machine starts to recirculate plasma through the bowl at increasing rates. The smallest particles, ie, platelets, ate the first to leave the bowl. Their high number causes the effluent line to turn foggy. The optical density of the fluid in the effluent line is monitored by the line sensor. A special algorithm then determines when to open and close the appropriate valves, as well as the optimum recirculation rate. 

D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading (Mass.), 1989. 2-263 W. Braun, G. Held, H.-P. Steinruck,... 

Algorithms for machine-generated syntheses by application of connective transforms to target structures containing appendages, medium-sized rings, etc. have been described. ... 

The above is an example of how direct algorithms may be formulated for methods involving electron correlation. It illustrates that it is not as straightforward to apply direct methods at the correlated level as at the SCF level. However, the steady increase in CPU performance, and especially the evolution of multiprocessor machines, favours direct (and semi-direct where some intermediate results are stored on disk) algorithms. Recently direct methods have also been implemented at the coupled cluster level. 

Pseudo-Code Implementation The Boltzman Machine Learning Algorithm proceeds in two phases (1) a positive, or learning, ph2 se and (2) a negative, or unlearning, phtise. It is summarized below in pseudo-code. It is assumed that the visible neurons are further subdivided into input and output sets as shown schematically in figure 10.8. 

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