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[Chauvin 1989] Y Chauvin, A Back-Propagation Algorithm with Optimal use of Hidden Units, in DS Touretzky (ed), Advances in Neural Information Processing Systems, Vol 1, 1989, pp 519-526 [Chauvin 1990] Y Chauvin, Dynamic Behavior of Constrained Back-Propagation Networks, in DS Touretzky (ed), Advances in Neural Information Processing Systems, Vol 2, 1990, pp 642 649 [Cibas et al 1996] T Cibas, F Fogelman Soulie, P Gallinari, S Raudys, Variable Selection with Neural Networks, Neurocomputing, Vol 12, 1996, pp 223 248 [Cichocki and Unbehauen 1993] A Cichocki, R Unbehauen, Neural Networks for Optimization and Signal Processing, Wiley, 1993 [Clerc and Kennedy 2002] M Clerc, J Kennedy, The Particle Swarm: Explosion, Stability, and Convergence in a Multi-Dimensional Complex Space, IEEE Transactions on Evolutionary Computation, Vol 6, pp 58 73, 2002.

Here the options have the following meanings: The -x option to tar means extract. The z option implies that you are uncompressing a tar archive where gzip compression has been used. The j option is needed if you are extracting an archive where bzip2 compression has been used.

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1181 Random, Personal and Systematic Errors There are three distinct types of error in the process of observation The simplest type of error is called random error Random errors are usually small deviations from the 'true value' of a measurement which occur by accident, by unforeseen jitter in the system, or some other influence By their nature, we are usually ignorant of the cause of random errors, otherwise it might be possible to eliminate them The important point about random errors is that they are distributed evenly about the mean value of the observation Indeed, it is usually assumed that they are distributed with an approximately normal or Gaussian profile about the mean This means that there are as many positive as negative deviations, and thus random errors can be averaged out by taking the mean of the observations.

Very commonly, programs that are distributed as source code are offered as compressed tar archives. When you have downloaded one of these, your first step is to copy it into a new directory somewhere and do either tar -zxvf or tar -jxvf to it. Then read the compilation and installation instructions (if you are lucky, the three commands ./configure followed by make followed by make install will do what you need). If you have a tar archive from which you need just a single file, you can do this:

It is tempting to believe that computers would not be susceptible to random errors After all, computers do not make mistakes However, this is an erroneous belief The measurer is not the only source of random errors A better way of expressing this is to say that random errors are a measure of the unpredictability of the measuring process Computer systems are also unpredictable, since they are constantly influenced by outside agents such as users and network requests The second type of error is a personal error This is an error which a particular experimenter adds to the data unwittingly There are many instances of this kind of error in the history of science In a computer controlled measurement process, this corresponds to any particular bias introduced through the use of specific software, or through the interpretation of the measurements.

If directory does not exist, it will be created with just the named file or files inside it. If you need to do this kind of task, Konqueror s ability to read inside archives is useful to remember; this is one area where the graphical tools can be a great help.

You can use tar to back up an entire directory tree to another location while preserving permissions and ownerships. Here s a sample command:

[Cloete and Ludik 1993] I Cloete, J Ludik, Increased Complexity Training, International Workshop on Artificial Neural Networks, in New Trends in Neural Computation, J Mira, J Cabestany, A Prieto (eds), in series Lecture Notes in Computer Science, Springer-Verlag, Berlin, 1993, pp 267 271 [Cloete and Ludik 1994a] I Cloete, J Ludik, Incremental Training Strategies, Proceedings of the International Conference on Artificial Neural Networks, Sorrento, Italy, Vol 2, May 1994, pp 743 746 [Cloete and Ludik 1994b] I Cloete, J Ludik, Delta Training Strategies, IEEE World Congress on Computational Intelligence, Proceedings of the International Joint Conference on Neural Networks, Orlando, USA, Vol 1, 1994, pp 295 298 [Cohn and Tesauro 1991] D Cohn, G Tesauro, Can Neural Networks do Better than the Vapnik-Chervonenkis Bounds , in R Lippmann, J Moody, DS Touretzky (eds), Advances in Neural Information Processing Systems, Vol 3, 1991, pp 911 917.

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