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NIPS
1994
13 years 7 months ago
Efficient Methods for Dealing with Missing Data in Supervised Learning
We present efficient algorithms for dealing with the problem of missing inputs (incomplete feature vectors) during training and recall. Our approach is based on the approximation ...
Volker Tresp, Ralph Neuneier, Subutai Ahmad
NCA
2011
IEEE
13 years 1 months ago
Privacy preserving Back-propagation neural network learning over arbitrarily partitioned data
—Neural Networks have been an active research area for decades. However, privacy bothers many when the training dataset for the neural networks is distributed between two parties...
Ankur Bansal, Tingting Chen, Sheng Zhong
IJCAI
1989
13 years 7 months ago
An Experimental Comparison of Symbolic and Connectionist Learning Algorithms
Despite the fact that many symbolic and connectionist (neural net) learning algorithms are addressing the same problem of learning from classified examples, very little Is known r...
Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. To...
IJCNN
2006
IEEE
14 years 8 days ago
Improving the Convergence of Backpropagation by Opposite Transfer Functions
—The backpropagation algorithm is a very popular approach to learning in feed-forward multi-layer perceptron networks. However, in many scenarios the time required to adequately ...
Mario Ventresca, Hamid R. Tizhoosh
NIPS
1993
13 years 7 months ago
Credit Assignment through Time: Alternatives to Backpropagation
Learning to recognize or predict sequences using long-term context has many applications. However, practical and theoretical problems are found in training recurrent neural networ...
Yoshua Bengio, Paolo Frasconi