16 research outputs found
Probabilistic Bounds on Complexity of Networks Computing Binary Classification Tasks
Complexity of feedforward networks computing binary classification tasks is investigated. To deal with unmanageably large number of these tasks on domains of even moderate sizes, a probabilistic model characterizing relevance of the classification tasks is introduced. Approximate measures of sparsity of networks computing randomly chosen functions are studied in terms of variational norms tailored to dictionaries of computational units. Probabilistic lower bounds on these norms are derived using the Chernoff-Hoeffding Bound on sums of independent random variables, which need not be identically distributed. Consequences of the probabilistic results on the choice of dictionaries of computational units are discussed
Mitigating Concept Drift via Rejection
Göpfert JP, Hammer B, Wersing H. Mitigating Concept Drift via Rejection. In: Kurkova V, Manolopoulos Y, Hammer B, Iliadis L, Maglogiannis I, eds. Artificial Neural Networks and Machine Learning â ICANN 2018. Proceedings, Part I. Lecture Notes in Computer Science. Vol 11139. Cham: Springer; 2018
Classification by Sparse Neural Networks
The choice of dictionaries of computational units
suitable for efficient computation of binary classification tasks is
investigated. To deal with exponentially growing sets of tasks with
increasingly large domains, a probabilistic model is introduced.
The relevance of tasks for a given application area is modeled by
a product probability distribution on the set of all binary-valued
functions. Approximate measures of network sparsity are studied
in terms of variational norms tailored to dictionaries of
computational units. Bounds on these norms are proven using
the Chernoff\u2013Hoeffding bound on sums of independent random
variables that need not be identically distributed. Consequences
of the probabilistic results for the choice of dictionaries of
computational units are derived. It is shown that when a priori
knowledge of a type of classification tasks is limited, then the
sparsity may be achieved only at the expense of large sizes of
dictionaries