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Sigmoidal or radial transfer functions do not guarantee the best generalization nor fast learning of neural networks. Families of parameterized transfer functions provide flexible decision borders. Networks based on such transfer functions should be small and accurate. Several possibilities of using transfer functions of different types in neural models are(More)
Incremental Net Pro (IncNet Pro) with local learning feature and statistically controlled growing and pruning of the netwo r k i s i n tro-duced. The architecture of the net is based on RBF networks. Extended Kalman Filter algorithm and its new fast version is proposed and used as learning algorithm. IncNet Pro is similar to the Resource A llocation Network(More)
This paper is an continuation of the accompanying paper with the same main title. The first paper reviewed instance selection algorithms, here results of empirical comparison and comments are presented. Several test were performed mostly on benchmark data sets from the machine learning repository at UCI. Instance selection algorithms were tested with neural(More)
Several methods were proposed to reduce the number of instances (vectors) in the learning set. Some of them extract only bad vectors while others try to remove as many instances as possible without significant degradation of the reduced dataset for learning. Several strategies to shrink training sets are compared here using different neural and machine(More)
—Learning methods with linear computational complexity O(nd) in number of samples and their dimension often give results that are better or at least not worse that more sophisticated and slower algorithms. This is demonstrated for many benchmark datasets downloaded from the UCI Machine Learning Repository. Results provided in this paper should be used as a(More)
We present a novel approach to meta-learning, which is not just a ranking of methods, not just a strategy for building model committees, but an algorithm performing a search similar to what human experts do when analyzing data, solving full scope of data mining problems. The search through the space of possible solutions is driven by special mechanisms of(More)