Bitcoin Forum

Have you ever heard about SVM? In machine learning, support vector machines are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.

SVM (but not neural networks) can be used instead to calculate a hash - in the end, from the same input parameters the result should always be the same. Who knows, maybe that big, self-learning network will create artificial intelligence? ;-) The network itself would have learned from the mistakes, and its aim would be to maximize the currency market ... What do you think?

they will all say it is a crap idea. just copy, pump and dump.

but yes... i said already something like this looks to me a good idea :) And u have 7GB to start working :)

Sounds interesting, more info on how to implement such algorythm is needed.

Skynet comes to mind.

From wikipedia:

In order to solve a given problem of supervised learning, one has to perform the following steps:

1.    Determine the type of training examples. Before doing anything else, the user should decide what kind of data is to be used as a training set. In the case of handwriting analysis, for example, this might be a single handwritten character, an entire handwritten word, or an entire line of handwriting.
2.    Gather a training set. The training set needs to be representative of the real-world use of the function. Thus, a set of input objects is gathered and corresponding outputs are also gathered, either from human experts or from measurements.
3.    Determine the input feature representation of the learned function. The accuracy of the learned function depends strongly on how the input object is represented. Typically, the input object is transformed into a feature vector, which contains a number of features that are descriptive of the object. The number of features should not be too large, because of the curse of dimensionality; but should contain enough information to accurately predict the output.
4.    Determine the structure of the learned function and corresponding learning algorithm. For example, the engineer may choose to use support vector machines or decision trees.
5.    Complete the design. Run the learning algorithm on the gathered training set. Some supervised learning algorithms require the user to determine certain control parameters. These parameters may be adjusted by optimizing performance on a subset (called a validation set) of the training set, or via cross-validation.
6.    Evaluate the accuracy of the learned function. After parameter adjustment and learning, the performance of the resulting function should be measured on a test set that is separate from the training set.

A wide range of supervised learning algorithms is available, each with its strengths and weaknesses. There is no single learning algorithm that works best on all supervised learning problems (see the No free lunch theorem).



Seems like a lot of human interaction is required.  Could be difficult to implement on a scale of thousands of miners.  All over the world.  Dozens of languages.  Thoughts?