Some embodiments are associated with a support vector machine having model parameters. According to some embodiments, a set of evaluation data may be received and a computer processor may automatically tune the model parameters during a training process using the set of evaluation data. The automatically tuned model parameters for the support vector machine may then be output directly from the training process.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method associated with a support vector machine having model parameters, comprising: receiving a set of evaluation data; automatically tuning, by a computer processor, model parameters during a training process using the set of evaluation data; and outputting, directly from the training process, the automatically tuned model parameters for the support vector machine. 2. The method of claim 1, wherein said automatic tuning is performed during a single phase of the training process. 3. The method of claim 1, further comprising: automatically rendering decisions using the support vector machine. 4. The method of claim 3, wherein the decisions are associated with at least one of: (i) classification, (ii) clustering, (iii) regression, (iv) anomaly detection, (v) association rules, (vi) reinforcement learning, (vii) structured prediction, (viii) feature learning, (ix) online learning, (x) semi-supervised learning, and (xi) grammar induction. 5. The method of claim 1, wherein said automatic tuning is performed by a set of i computational stations, where i is an integer greater than 1. 6. The method of claim 5, wherein said automatic tuning is performed by iteratively performing the following phases until convergence is achieved: a distribution phase to minimize a loss function associated with the support vector machine by distributing subsets of the evaluation data to the i computational stations; a collecting phase to enforce regularization and update shared model parameters; and a tuning phase that uses the set of evaluation data to update a trade-off parameter C. 7. The method of claim 6, wherein the distribution phase updates decoupled parameters, wherein w(i) represents a decision boundary for each subset of evaluation data x(i) distributed to an ith slave computational station as follows: w t + 1  (  ) = arg   min w  (  )   Cl  ( α i  w  (  ) · φ  ( x  (  ) ) ) + 〈 α i , w  (  ) - z 〉 + λ 2   w  (  ) - z  2 8. The method of claim 6, wherein the collection phase collects distributed weighting parameters at a master computational station and updates regularization as follows: z t + 1 = arg   min z   β  1 2   z  + ∑ i   ( 〈 α t , w C  (  ) - z 〉 + λ 2   w C  (  ) - z  2 ) 9. The method of claim 6, wherein the tuning phase updates the trade-off parameter C as follows: C t + 1 = C t - γ ( ∑ ( x , y ) ∈  D v  l  ( yz · φ  ( x ) ) + 2   β  ∂ R  ( C t ) ∂ ( C t ) ) 10. A non-transitory, computer-readable medium storing instructions that, when executed by a computer processor, cause the computer processor to perform a method associated with a support vector machine having model parameters, the method comprising: receiving a set of evaluation data; automatically tuning, by the computer processor, the model parameters during a training process using the set of evaluation data; and outputting, directly from the training process, the automatically tuned model parameters for the support vector machine. 11. The medium of claim 10, wherein said automatic tuning is performed during a single phase of the training process. 12. The medium of claim 10, wherein the method further comprises: automatically rendering decisions using the support vector machine, wherein the decisions are associated with at least one of: (i) classification, (ii) clustering, (iii) regression, (iv) anomaly detection, (v) association rules, (vi) reinforcement learning, (vii) structured prediction, (viii) feature learning, (ix) online learning, (x) semi-supervised learning, and (xi) grammar induction. 13. The medium of claim 10, wherein said automatic tuning is performed by a set of i computational stations, where i is an integer greater than 1, by iteratively performing the following phases until convergence is achieved: a distribution phase to minimize a loss function associated with the support vector machine by distributing subsets of the evaluation data to the i computational stations; a collecting phase to enforce regularization and update shared model parameters; and a tuning phase that uses the set of evaluation data to update a trade-off parameter C. 14. The medium of claim 13, wherein the distribution phase updates decoupled parameters, wherein w(i) represents a decision boundary for each subset of evaluation data x(i) distributed to an ith slave computational station as follows: w t + 1  (  ) = arg   min w  (  )   Cl  ( α i  w  (  ) · φ  ( x  (  ) ) ) + 〈 α i , w  (  ) - z 〉 + λ 2   w  (  ) - z  2 15. The medium of claim 13, wherein the collection phase collects distributed weighting parameters at a master computational station and updates regularization as follows: z t + 1 = arg   min z   β  1 2   z  + ∑ i   ( 〈 α t , w C  (  ) - z 〉 + λ 2   w C  (  ) - z  2 ) 16. The medium of claim 13, wherein the tuning phase updates the trade-off parameter C as follows: C t + 1 = C t - γ ( ∑ ( x , y ) ∈  D v  l  ( yz · φ  ( x ) ) + 2   β  ∂ R  ( C t ) ∂ ( C t ) ) 17. A system, comprising: a storage device to store a set of evaluation data; and a computer system coupled to the storage device to: (i) automatically tune the model parameters during a training process using the set of evaluation data, and (ii) output, directly from the training process, the automatically tuned model parameters for the support vector machine. 18. The system of claim 17, wherein said automatic tuning is performed during a single phase of the training process. 19. The system of claim 17, wherein the method further comprises: automatically rendering decisions using the support vector machine, wherein the decisions are associated with at least one of: (i) classification, (ii) clustering, (iii) regression, (iv) anomaly detection, (v) association rules, (vi) reinforcement learning, (vii) structured prediction, (viii) feature learning, (ix) online learning, (x) semi-supervised learning, and (xi) grammar induction. 20. The system of claim 17, wherein said automatic tuning is performed by a set of i computational stations, where i is an integer greater than 1, by iteratively performing the following phases until convergence is achieved: a distribution phase to minimize a loss function associated with the support vector machine by distributing subsets of the evaluation data to the i computational stations; a collecting phase to enforce regularization and update shared model parameters; and a tuning phase that uses the set of evaluation data to update a trade-off parameter C.