Michael Schmitt Complexity of Learning for Networks of Spiking Neurons with Nonlinear Synaptic Interactions. [Citation Graph (0, 0)][DBLP] ICANN, 2001, pp:247-252 [Conf]
Michael Schmitt Product Unit Neural Networks with Constant Depth and Superlinear VC Dimension. [Citation Graph (0, 0)][DBLP] ICANN, 2001, pp:253-258 [Conf]
Michael Schmitt Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks. [Citation Graph (0, 0)][DBLP] NIPS, 1999, pp:328-334 [Conf]
Michael Schmitt On Computing Boolean Functions by a Spiking Neuron. [Citation Graph (0, 0)][DBLP] Ann. Math. Artif. Intell., 1998, v:24, n:1-4, pp:181-191 [Journal]
Michael Schmitt Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks [Citation Graph (0, 0)][DBLP] Electronic Colloquium on Computational Complexity (ECCC), 2000, v:7, n:2, pp:- [Journal]
Michael Schmitt On the Complexity of Computing and Learning with Multiplicative Neural Networks [Citation Graph (0, 0)][DBLP] Electronic Colloquium on Computational Complexity (ECCC), 2000, v:7, n:86, pp:- [Journal]
Michael Schmitt Neural Networks with Local Receptive Fields and Superlinear VC Dimension [Citation Graph (0, 0)][DBLP] Electronic Colloquium on Computational Complexity (ECCC), 2001, v:8, n:45, pp:- [Journal]
Michael Schmitt On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions [Citation Graph (0, 0)][DBLP] Electronic Colloquium on Computational Complexity (ECCC), 2004, v:, n:033, pp:- [Journal]
Michael Schmitt Some dichotomy theorems for neural learning problems [Citation Graph (0, 0)][DBLP] Electronic Colloquium on Computational Complexity (ECCC), 2004, v:, n:075, pp:- [Journal]
Michael Schmitt On the Sample Complexity for Nonoverlapping Neural Networks [Citation Graph (0, 0)][DBLP] Electronic Colloquium on Computational Complexity (ECCC), 1999, v:6, n:5, pp:- [Journal]
Michael Schmitt On the Sample Complexity for Nonoverlapping Neural Networks. [Citation Graph (0, 0)][DBLP] Machine Learning, 1999, v:37, n:2, pp:131-141 [Journal]
Michael Schmitt On the Complexity of Computing and Learning with Multiplicative Neural Networks. [Citation Graph (0, 0)][DBLP] Neural Computation, 2002, v:14, n:2, pp:241-301 [Journal]
Michael Schmitt Neural Networks with Local Receptive Fields and Superlinear VC Dimension. [Citation Graph (0, 0)][DBLP] Neural Computation, 2002, v:14, n:4, pp:919-956 [Journal]
Michael Schmitt Descartes' Rule of Signs for Radial Basis Function Neural Networks. [Citation Graph (0, 0)][DBLP] Neural Computation, 2002, v:14, n:12, pp:2997-3011 [Journal]
Michael Schmitt On the Capabilities of Higher-Order Neurons: A Radial Basis Function Approach. [Citation Graph (0, 0)][DBLP] Neural Computation, 2005, v:17, n:3, pp:715-729 [Journal]
Michael Schmitt Identification Criteria and Lower Bounds for Perceptron-LikeLearning Rules. [Citation Graph (0, 0)][DBLP] Neural Computation, 1998, v:10, n:1, pp:235-250 [Journal]
Michael Schmitt On using the Poincaré polynomial for calculating the VC dimension of neural networks. [Citation Graph (0, 0)][DBLP] Neural Networks, 2001, v:14, n:10, pp:1465-0 [Journal]