Also mutual neighbor distance (MND), Minkowski metric, Hausdorff distance, conceptual similarity, normalized Google distance, KL divergence, Spearmanâ€™s rank correlation, and Lin similarity. (Not all of these are vector based.)

I highly recommend Pattern Classification by Duda, Hart, and Stork for further reading. It is extensively cited.

If you're actually using a backpropagation network with 1558 input nodes and only 3279 samples, then the training time is the least of your problems: Even if you have a very small network with only one hidden layer containing 10 neurons, you have 1558*10 weights between the input layer and the hidden layer. How can you expect to get a good estimate for 15580 degrees of freedom from only 3279 samples? (And that simple calculation doesn't even take the "curse of dimensionality" into account)

You have to analyze your data to find out how to optimize it. Try to understand your input data: Which (tuples of) features are (jointly) statistically significant? (use standard statistical methods for this) Are some features redundant? (Principal component analysis is a good stating point for this.) Don't expect the artificial neural network to do that work for you.

Also: remeber Duda&Hart's famous "no-free-lunch-theorem": No classification algorithm works for every problem. And for any classification algorithm X, there is a problem where flipping a coin leads to better results than X. If you take this into account, deciding what algorithm to use before analyzing your data might not be a smart idea. You might well have picked the algorithm that actually performs worse than blind guessing on your specific problem! (By the way: Duda&Hart&Storks's book about pattern classification is a great starting point to learn about this, if you haven't read it yet.)

Indeed they are. I see a bayesian network as a neural network applying the Baye's Theorem on large scale, but I don't remember details. I know where you can find them and I recommend this book for that.

Also mutual neighbor distance (MND), Minkowski metric, Hausdorff distance, conceptual similarity, normalized Google distance, KL divergence, Spearmanâ€™s rank correlation, and Lin similarity. (Not all of these are vector based.)

I highly recommend Pattern Classification by Duda, Hart, and Stork for further reading. It is extensively cited.

If you're actually using a backpropagation network with 1558 input nodes and only 3279 samples, then the training time is the least of your problems: Even if you have a very small network with only one hidden layer containing 10 neurons, you have 1558*10 weights between the input layer and the hidden layer. How can you expect to get a good estimate for 15580 degrees of freedom from only 3279 samples? (And that simple calculation doesn't even take the "curse of dimensionality" into account)

You have to analyze your data to find out how to optimize it. Try to understand your input data: Which (tuples of) features are (jointly) statistically significant? (use standard statistical methods for this) Are some features redundant? (Principal component analysis is a good stating point for this.) Don't expect the artificial neural network to do that work for you.

Also: remeber Duda&Hart's famous "no-free-lunch-theorem": No classification algorithm works for every problem. And for any classification algorithm X, there is a problem where flipping a coin leads to better results than X. If you take this into account, deciding what algorithm to use before analyzing your data might not be a smart idea. You might well have picked the algorithm that actually performs worse than blind guessing on your specific problem! (By the way: Duda&Hart&Storks's book about pattern classification is a great starting point to learn about this, if you haven't read it yet.)

Indeed they are. I see a bayesian network as a neural network applying the Baye's Theorem on large scale, but I don't remember details. I know where you can find them and I recommend this book for that.