Method of training a neural network and a neural network trained according to the method

• US 20050149463A1
• Filed: 10/28/2004
• Published: 07/07/2005
• Est. Priority Date: 04/29/2002
• Status: Abandoned Application

• Current Assignee: Cerebrus Solutions Ltd (Neural Technologies Limited)
• Original Assignee: Cerebrus Solutions Ltd (Neural Technologies Limited)
• Inventors: Bolt, George, McLachlan, Alan, Manslow, John
• Application Number:
  US10/976,167
• Publication Number:
  US 20050149463A1
• Time in Patent Office: Days
• US Class Current: 706/20
• CPC Class Codes: G06N 3/08 Learning methods

Abstract

A neural network comprises trained interconnected neurons. The neural network is configured to constrain the relationship between one or more inputs and one or more outputs of the neural network so the relationships between them are consistent with expectations of the relationships; and/or the neural network is trained by creating a set of data comprising input data and associated outputs that represent archetypal results and providing real exemplary input data and associated output data and the created data to neural network. The real exemplary output data and the created associated output data is compared to the actual output of the neural network, which is adjusted to create a best fit to the real exemplary data and the created data.

Claims

1. A method of training a neural network having one or more outputs, each output representing numeric or non-numeric values and when only small sets of examples are available for training, the method comprising:

numerically encoding each non-numeric value such that the uniqueness and adjacency relationships between them are preserved;

constraining the relationship between one or more inputs and one or more outputs that the neural network learns so that it is consistent with an expected relationship between the one or more inputs and the one or more outputs;

creating a set of data comprising input data and associated outputs that represent archetypal results;

providing real exemplary input data and associated output data and the created data to the neural network;

comparing real exemplary output data and the created associated output data to the actual output of the neural network; and

adjusting the neural network to create a best fit to the real exemplary data and the created data.

2. A neural network, comprising:

a plurality of inputs and one or more outputs which produce an output dependant on data received by the input according to training of interconnections between the inputs, hidden neurons and the outputs,

wherein interconnections are trained such that the relationship between the inputs and the outputs is constrained according to the expectations of the relationship between the inputs and the outputs,

wherein one or more output neurons produce a numeric preliminary output, the preliminary output being manipulated to produce a final output,

wherein during training of the neural network each possible non-numeric final output is numerically encoded into a training preliminary output such that the uniqueness and adjacency relations between each non-numeric final output value is preserved, and

wherein, in use, the preliminary output is converted to an estimated nonnumeric final output based on the nearest numerically encoded equivalent final output used in training the neural network.

3. A neural network, comprising:

trained interconnected neurons,

wherein one or more neurons produce a numeric preliminary output, the preliminary output being manipulated to produce a final output,

wherein during training of the neural network each possible non-numeric final output is numerically encoded into a training preliminary output such that the uniqueness and adjacency relations between each non-numeric final output are preserved, and

wherein, in use, the preliminary output is converted to an estimated nonnumeric final output.

4. A neural network according to claim 3, wherein the preliminary output comprises one or more scalars, and wherein the final output is based on the nearest numerically encoded equivalent final output used in training the neural network.

5. A neural network according to claim 3, wherein the preliminary output is a probability density over the range of possible network outputs.

6. A neural network according to claim 5, wherein the probability density is decoded by computing the probability of each category from the proportion of the probability mass that lies within the range of each rating, and wherein the range of a rating is defined as all values of the output that are closer to the encoded rating than any other.

7. A method of training a neural network for improved robustness when only small sets of examples are available for training, the method comprising:

creating a set of data comprising input data and associated outputs that represent archetypal results;

providing real exemplary input data and associated output data and the created data to the neural network;

comparing real exemplary output data and the created associated output data to the actual output of the neural network; and

adjusting the neural network to create a best fit to the real exemplary data and the created data.

8. A method of training a neural network for improved robustness when only small sets of examples are available for training, the method comprising:

constraining the relationship between one or more inputs and one or more outputs of the neural network so that the relationship is consistent with an expected relationship between the one or more inputs and the one or more outputs.

9. A method according to claim 8, wherein the constraint on the relationship to be satisfied is based on prior knowledge of the relationships between certain inputs and the outputs desired of the neural network.

10. A method according to claim 8, wherein the constraint is such that when a certain input changes the output monotonically changes.

11. A method according to claim 8, wherein the neural network being trained has one or more neurons with monotonic activation functions and the signs of the weights of the connections between a layer of input neurons, one or more layers of hidden neurons and a layer of output neurons determines whether the neural network output is positively or negatively monotonic with respect to each input.

12. A method according to claim 11, wherein the signs of the weights connecting two or more neurons are fixed by defining the weights in terms of positive functions of one or more dummy weights.

13. A method according to claim 11, wherein the signs of the weights connecting two or more neurons are fixed by defining the weights in terms of negative functions of one or more dummy weights.

14. A method according to claim 11, wherein the positive functions, used to derive the constrained weights from the dummy weights, include an exponential function.

15. A method according to claim 13, wherein the negative functions, used to derive the constrained weights from the dummy weights, are minus one times an exponential function.

16. A method according to either claim 12, wherein the neural network is trained by applying a standard unconstrained optimization technique that is used for training simultaneously all weights that do not need to be constrained and the dummy weights.

17. A method according to claim 16, wherein the neural network'"'"'s constrained weights are computed from their dummy weights.

18. A method according to claim 12, wherein the neural network may be used to estimate business credit scores as any other network would, without special consideration as to which weights were constrained and unconstrained during training.

19. A neural network, comprising:

a plurality of inputs and one or more outputs which produce an output dependant on data received by the input according to training of interconnections between the input, hidden neurons and the outputs,

wherein interconnections are trained such that the relationship between the inputs and the outputs of the neural network is constrained, according to expectations of the relationship between the inputs and the outputs.

20. A neural network according to claim 19, wherein one or more of the neurons have monotonic activation functions determined by prior knowledge of the relationships between certain inputs and certain outputs of the neural network.

21. A neural network according to claim 20, wherein the interconnected neurons include a layer of input neurons, one or more layers of hidden neurons and a layer of output neurons, and wherein certain input neurons are not connected to the same hidden neurons where it is known that certain inputs are to affect the output of the network independently.

22. A neural network according to claim 20, wherein the interconnected neurons include a layer of input neurons, one or more layers of hidden neurons, and a layer of output neurons, and wherein the weights between the hidden neurons and the output neurons that directly or indirectly lie between an output that must change monotonically with respect to one or more inputs, are of the same sign.

23. A neural network according to claim 22, wherein the weights between each input neuron and all hidden neurons that are connected directly or indirectly to an output that change monotonically with the input are of the same sign.

24. A neural network according to claim 22, wherein the sign of the weights between the input layer and the hidden layer determine whether the neural network output is positively or negatively monotonic with respect to each input.

25. A neural network according to claim 24, wherein the neural network is a Bayesian neural network, where a posterior probability density over the neural network'"'"'s weights is the result of training.

26. A neural network according to claim 25, wherein the posterior probability density is used to provide an indication of how consistent different combinations of values of the weights are with the information in the training samples and the prior probability density.

27. A neural network according to claim 26, wherein prior knowledge about which combinations of weight values are likely to produce networks that produce good credit score estimates is used by expressing the prior knowledge as a prior probability density over the values of the neural network'"'"'s weights.

28. A neural network according to claim 27, wherein the prior probability density is chosen to be a Gaussian distribution centered at the point where all weights are zero.

29. A neural network according to claim 28, wherein the additional prior knowledge that certain weights are either positive or negative, by setting the prior probability density to zero for any combination of weight values that violate the constraints required to impose the desired monotonicity constraints.

30. A method of training a neural network when only small sets of examples are available for training, the comprising:

constraining the relationship between one or more inputs and one or more outputs so that the relationship between them is consistent with an expected relationship between the one or more inputs and the one or more outputs;

creating a set of data comprising input data and associated outputs that represent archetypal results;

providing real exemplary input data and associated output data and the created data to the neural network;

comparing real exemplary output data and the created associated output data to the actual output of the neural network; and

adjusting the neural network to create a best fit to the real exemplary data and the created data, where the best fit is determined in accordance with normal neural network training practice.

31. A system for training a neural network having one or more outputs, each output representing numeric or non-numeric values and when only small sets of examples are available for training, the system comprising:

means for numerically encoding each non-numeric value such that the uniqueness and adjacency relationships between them are preserved;

means for constraining the relationship between one or more inputs and one or more outputs that the neural network learns so that it is consistent with an expected relationship between the one or more inputs and the one or more outputs;

means for creating a set of data comprising input data and associated outputs that represent archetypal results;

means for providing real exemplary input data and associated output data and the created data to the neural network;

means for comparing real exemplary output data and the created associated output data to the actual output of the neural network; and

means for adjusting the neural network to create a ‘

best fit’

to the real exemplary data and the created data.