Peter G. Anderson Roger S. Gaborski Ming Ge Sanjay Raghavendra Mei-Ling Lung Abstract We present a novel training algorithm for a feed forward neural network with a single hidden layer of nodes (i.e., two layers of connection weights). Our algorithm is capable of training networks for hard problems, such as the classic two-spirals problem. The weights in the rst layer are determined using a qua...

Adverse selection famously leads to the crowding out of socially beneficial trades. We show that even more trades may be simultaneously crowded in. The reason is that, in absence complete unravelling, “lemons” fetch under adverse selection. It demonstrated how these “bad” occur insurance, credit and used-car markets, some policy implications are discussed.

Bayesian networks are constructed under a conditional independency assumption. This assumption however does not necessarily hold in practice and may lead to loss of accuracy. We previously proposed a hidden node methodology whereby Bayesian networks are adapted by the addition of hidden nodes to model the data dependencies more accurately. Empirical results in a computer vision application to c...