Abstract: A system for training classifiers in multiple categories through an active learning system, including a computer having a memory and a processor, the processor programmed to: train an initial set of m binary one-versus-all classifiers, one for each category in a taxonomy, on a labeled dataset of examples stored in a database coupled with the computer; uniformly sample up to a predetermined large number of examples from a second, larger dataset of unlabeled examples stored in a database coupled with the computer; order the sampled unlabeled examples in order of informativeness for each classifier; determine a minimum subset of the unlabeled examples that are most informative for a maximum number of the classifiers to form an active set for learning; and use editorially-labeled versions of the examples of the active set to re-train the classifiers, thereby improving the accuracy of at least some of the classifiers.

Abstract: A system for training classifiers in multiple categories through an active learning system, including a computer having a memory and a processor, the processor programmed to: train an initial set of m binary one-versus-all classifiers, one for each category in a taxonomy, on a labeled dataset of examples stored in a database coupled with the computer; uniformly sample up to a predetermined large number of examples from a second, larger dataset of unlabeled examples stored in a database coupled with the computer; order the sampled unlabeled examples in order of informativeness for each classifier; determine a minimum subset of the unlabeled examples that are most informative for a maximum number of the classifiers to form an active set for learning; and use editorially-labeled versions of the examples of the active set to re-train the classifiers, thereby improving the accuracy of at least some of the classifiers.

Abstract: A network based advertisement system includes an optimizer configured to forecast a supply of opportunities, forecast a supply of guaranteed contracts, and forecast a supply of non-guaranteed contracts. Each opportunity represents a user visiting a webpage. Each guaranteed contract guarantees the matching of an advertisement to a number of opportunities. Each non-guaranteed contract guarantees a user event associated with an advertisement. The optimizer then generates a plan for matching contracts to opportunities based on the forecasted supply of opportunities, the forecasted supply of guaranteed contracts, the forecasted supply of non-guaranteed contracts, and an objective function that balances a group of parameters that define the representativeness of contracts, a cost associated with not serving non-guaranteed contracts, and performance objectives associated with contracts.