A computer-implemented method of automating inductive bias selection includes a computer receiving a plurality of examples, each example providing a plurality of feature-value pairs. The computer constructs an inductive bias dataset which correlates each respective example in the plurality of examples with numerical indications of training quality. The numerical indications of training quality for each respective example are generated by creating a plurality of models, with each model corresponding to a distinct set of inductive biases. The training quality for each respective model is evaluated when applied to the respective example. The computer uses the inductive bias dataset to select a plurality of inductive biases for application to one or more new datasets.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method of automating inductive bias selection, the method comprising: receiving, by a computer, a plurality of examples, each example providing a plurality of feature-value pairs; constructing, by the computer, an inductive bias dataset which correlates each respective example in the plurality of examples with numerical indications of training quality, wherein the numerical indications of training quality for each respective example are generated by: creating a plurality of models, each model corresponding to a distinct set of inductive biases, and evaluating training quality for each respective model when applied to the respective example; using, by the computer, the inductive bias dataset to select a plurality of inductive biases for application to one or more new datasets. 2. The method of claim 1, wherein the distinct set of inductive biases comprise a first bias corresponding to a model type. 3. The method of claim 1, wherein the distinct set of inductive biases comprise one or more second biases corresponding to model parameters corresponding to a respective model type. 4. The method of claim 1, wherein the distinct set of inductive biases comprise a first bias corresponding to an ensemble modeling technique. 5. The method of claim 1, wherein the distinct set of inductive biases comprise a first bias corresponding a measure of conservativeness with respective to model applicability and model accuracy. 6. The method of claim 1, wherein the distinct set of inductive biases comprise a first bias corresponding to an indication of sampling method. 7. The method of claim 1, wherein the distinct set of inductive biases comprise a first bias corresponding to an indication of technique for imputing missing data for particular data types. 8. The method of claim 1, wherein the plurality of examples comprise time-series data and the distinct set of inductive biases comprise one or more wavelet transformations. 9. The method of claim 1, wherein the distinct set of inductive biases comprise a one or more nominal biases corresponding to an external data source. 10. The method of claim 9, further comprising: using the inductive bias dataset to score a plurality of available external data sources; and generating a model which applies each of the plurality of available external data sources in proportion with its respective score. 11. A computer-implemented method of performing recursive learning based on a plurality of feature values, the method comprising: generating, by a computer, a plurality of models for a plurality of input features; receiving, by the computer, a plurality of examples, each example providing a plurality of input feature values corresponding to the plurality of input features; applying, by the computer, a recursive learning process to each respective example included in the plurality of examples, the recursive learning process comprising: selecting a preferred model from the plurality of models, the preferred model providing a lowest predicted error value when applied to the respective example, and associating the respective example with the preferred model; receiving a new example comprising a plurality of new input feature values; using, by the computer, the plurality of new input feature values to select a similar example from the plurality of examples; identifying, by the computer, one or more corresponding preferred models associated with the similar example; and applying, by the computer, the one or more corresponding preferred models to the new example. 12. The method of claim 11, further comprising: associating each of the plurality of models with a score indicative of model quality; identifying the one or more corresponding preferred models associated with the similar example based on the score of each of the one or more corresponding preferred models; averaging results generated by applying the one or more corresponding preferred models to the new example to yield a final prediction. 13. The method of claim 11, further comprising: creating a plurality of recursive learners, each respective recursive learner corresponding to distinct set of parameters; creating a high-order recursive learner configured to select from the plurality of recursive learners based on feature-value pairs associated with a particular example; and using the high-order recursive learner to select a distinct recursive learner from the plurality of recursive learners based on the plurality of new input feature values, wherein the distinct recursive learner uses the plurality of new input feature values to select the similar example from the plurality of examples and identifies the one or more corresponding preferred models associated with the similar example. 14. The method of claim 13, wherein each of the plurality of recursive learners corresponds to a distinct learning method. 15. The method of claim 14, wherein at least one of the plurality of recursive learners corresponds to a decision tree learning method and at least one of the plurality of recursive learners corresponds to a neural network learning method. 16. The method of claim 11, further comprising: partitioning the plurality of new input feature values into a first set of input feature values and a second set of input feature values; using, by the computer, the first set of input feature values to select a first similar example from the plurality of examples; identifying, by the computer, one or more first preferred models associated with the first similar example; applying, by the computer, the one or more first preferred models to the first set of input feature values; using, by the computer, the second set of input feature values to select a second similar example from the plurality of examples; identifying, by the computer, one or more second preferred models associated with the second similar example; and applying, by the computer, the one or more second preferred models to the second set of input feature values. 17. The method of claim 16, further comprising generating a final prediction by averaging (i) first results generated by applying the one or more first preferred models to the first set of input feature values and (ii) second results generated by applying the one or more second preferred models to the second set of input feature values. 18. A modeling computing system comprising: a processor configured to retrieve a plurality of examples from an example database and execute a plurality of modeling components comprising: a model generation component configured to generate a plurality of models, each model corresponding to a specified set of inductive biases; an inductive bias dataset component configured to construct an inductive bias dataset which correlates each respective example in the plurality of examples with numerical indications of training quality, wherein the numerical indications of training quality for each respective example are generated by: creating a plurality of models, each model corresponding to a distinct set of inductive biases, and evaluating training quality for each respective model when applied to the respective example. 19. The modeling computing system of claim 18, wherein the inductive bias dataset component is further configured to use the inductive bias dataset to select a plurality of inductive biases for application to one or more new datasets. 20. The modeling computing system of claim 18, wherein the plurality of modeling components further comprise a recursive learning component configured to apply a recursive learning process to each respective example included in the plurality of examples, the recursive learning process comprising: selecting a preferred model from the plurality of models, the preferred model providing a lowest predicted error value when applied to the respective example, and associating the respective example with the preferred model.