A learning framework and methods of machine learning are disclosed. Specifically, an Analytical Neural Network Intelligent Interface (ANNII) is disclosed that includes the ability to analyze incoming data in substantially real-time and determine whether or not the data is statistically anomalous data. Learning models can then be updated depending upon whether or not the data is determined to be statistically anomalous data or not.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: receiving a data input at a computer learning framework; decomposing the data input into elemental pieces; providing the elemental pieces of the data input to a statistical analysis layer where the elemental pieces are compared to one or more statistical models to determine if the data input corresponds to a statistically anomalous event; and at least one of marking the data input as statistically anomalous and updating the one or more statistical models. 2. The method of claim 1, wherein decomposing the data input comprises extracting at least one of a variable, variable value, parameter value, and header value from the data input. 3. The method of claim 1, wherein the data input corresponds to any one of the following machine languages: C, C+, C#, Object C, Java, Encog, Fortran, Python, PHP, PERL, Ruby Rails, and Open CL. 4. The method of claim 1, further comprising: executing the statistical analysis layer in a High Performance Computing (HPC) environment. 5. The method of claim 1, wherein the one or more statistical models include at least one of the following: regression analysis; cluster analysis/spread spectrum analysis; Bayesian Probability Analysis (Acyclic); Markov Networks; Relevance Analysis; Heuristic Modeling/Meteheuristic; Simulated Annealing; Genetic Algorithms; Statistical Analysis; Support Vectors, Monte Carlo Simulators; and combinations thereof. 6. The method of claim 1, wherein the data input is provided to a virtual machine for further analysis in the event that the data input is identified as statistically anomalous. 7. The method of claim 1, wherein the data input is identified as statistically anomalous according to the following algorithm: if X ⊂ T Associationrule:XYhereX⊂I,Y⊂IandX∩Y=ØSupp(X ⊂ Y)=number of transactions in D contain (X ∪ Y), where X is a subset of I; D is a database of transactions; T ε D is a transaction for T ⊂ I; and TID is a unique identifier, associated with each T. 8. A non-transitory computer-readable medium comprising processor-executable instructions that, when executed by a processor, perform a method, the method comprising: receiving a data input at a computer learning framework; decomposing the data input into elemental pieces; providing the elemental pieces of the data input to a statistical analysis layer where the elemental pieces are compared to one or more statistical models to determine if the data input corresponds to a statistically anomalous event; and at least one of marking the data input as statistically anomalous and updating the one or more statistical models. 9. The computer-readable medium of claim 8, wherein decomposing the data input comprises extracting at least one of a variable, variable value, parameter value, and header value from the data input. 10. The computer-readable medium of claim 8, wherein the data input corresponds to any one of the following machine languages: C, C+, C#, Object C, Java, Encog, Fortran, Python, PHP, PERL, Ruby Rails, and Open CL. 11. The computer-readable medium of claim 8, wherein the method further comprises: executing the statistical analysis layer in a High Performance Computing (HPC) environment. 12. The computer-readable medium of claim 8, wherein the one or more statistical models include at least one of the following: regression analysis; cluster analysis/spread spectrum analysis; Bayesian Probability Analysis (Acyclic); Markov Networks; Relevance Analysis; Heuristic Modeling/Meteheuristic; Simulated Annealing; Genetic Algorithms; Statistical Analysis; Support Vectors, Monte Carlo Simulators; and combinations thereof. 13. The computer-readable medium of claim 8, wherein the data input is provided to a virtual machine for further analysis in the event that the data input is identified as statistically anomalous. 14. The computer-readable medium of claim 8, wherein the data input is identified as statistically anomalous according to the following algorithm: if X ⊂ T Associationrule:XYhereX⊂I,Y⊂IandX∩Y=ØSupp(X ∪ Y)=number of transactions in D contain (X ⊂ Y), where X is a subset of I; D is a database of transactions; T ε D is a transaction for T⊂ I; and TID is a unique identifier, associated with each T. 15. A machine-learning system, comprising: a microprocessor configured to execute instructions stored in computer memory; and computer memory including: a computer learning framework that, when executed by the processor, is configured to receive a data input, decompose the data input into elemental pieces, provide the elemental pieces of the data input to a statistical analysis layer where the elemental pieces are compared to one or more statistical models to determine if the data input corresponds to a statistically anomalous event, and at least one of mark the data input as statistically anomalous and update the one or more statistical models. 16. The machine-learning system of claim 15, wherein decomposing the data input comprises extracting at least one of a variable, variable value, parameter value, and header value from the data input. 17. The machine-learning system of claim 15, wherein the data input corresponds to any one of the following machine languages: C, C+, C#, Object C, Java, Encog, Fortran, Python, PHP, PERL, Ruby Rails, and Open CL. 18. The machine-learning system of claim 15, wherein the computer learning framework is executed in a High Performance Computing (HPC) environment. 19. The machine-learning system of claim 15, wherein the one or more statistical models include at least one of the following: regression analysis; cluster analysis/spread spectrum analysis; Bayesian Probability Analysis (Acyclic); Markov Networks; Relevance Analysis; Heuristic Modeling/Meteheuristic; Simulated Annealing; Genetic Algorithms; Statistical Analysis; Support Vectors, Monte Carlo Simulators; and combinations thereof. 20. The machine-learning system of claim 15, wherein the data input is provided to a virtual machine for further analysis in the event that the data input is identified as statistically anomalous.