Particle Swarm
نویسنده
چکیده
Many training algorithms (like gradient descent, for example) use random initial weights. These algorithms are rather sensitive to their starting position in the error space, which is represented by their initial weights. This paper shows that the training performance can be improved signiicantly by using a Particle Swarm Optimizer (PSO) to initialize the weights, rather than random initialization.
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تاریخ انتشار 1999