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computer experimentsin this experiment we use computer simulations to investigate the som algorithm applied to a
principal-components analysis constrained-optimization approachin section 84 we used perturbation theory to derive the
hebbian-based principal-components analysisconstruct a signal-flow graph to represent the vector-valued eqs 887 and
the ordinary differential equation approach to convergence analysis described in section 85 does not apply directly to
in the presentation of a data set made up of an aggregate of several clusters we may say that for the clusters to be
in developing the approximate update formula of eq955 for the weight vector of the kernel som algorithm we justified
in computational terms the coherent ica algorithm shares two features that are similar to two features of the fastica
coherent icain combining the info max and imax contributions to the objective function jwalpha wb we bypassed the need
the fastica algorithm is claimed to be much faster than other ica algorithms namelythe natural-gradient algorithm and
independent-components analysis may be used as a preprocessing step for approximate data analysis before detection and
infomax principleconsider two channels whose outputs are represented by the random variables x and y the requirement is
to derive eq 1050 on the relationship between mutual information and a copulas entropy we used a direct approach
in practice an algorithmic implementation of independent-components analysis can go only for as statistically
in this problem we explore the use of the kullback-leibler divergence kld to derive a supervised-learning algorithm for
simulationto relieve some programming burden project 5 is a group project that two students can form a group to do and
figure p1220 depicts a neural-network-based scheme for approximating the target q-factor denoted by qtarget i alpha w
consider a data set that is a mixture of gaussian distributions in what way does the use of deterministic annealing
consider the markov chain depicted in fig p113 which is reducible identify the classes of states contained in this
figure p112 shows the state transition diagram for the random walk process where the transition probability p is
calculate the steady-state probabilities of the markov chain shown in fig
consider the example of a markov chain shown in fig p115 using this example verify the validity of the
in this problem we consider the use of simulated annealing for solving the traveling-salesman problem tsp you are given
simulation techniquesthe metropolis algorithm and the gibbs sampler represent two alternative techniques for simulating
deterministic annealingin section 1110 we developed the idea of deterministic annealing using an information theoretic