Abstract: Methods and apparatus are described for evaluating a binary classification system operable to classify each of a plurality of events as a first event type or a second event type. At least some of the events of the first event type are independently verifiable with reference to verification data. The binary classification system is susceptible to a first error type in which events of the first event type are classified as the second event type, and a second error type in which events of the second event type are classified as the first event type. Operation of a first configuration of the binary classification system is evaluated with reference to an objective function. The objective function is derived by expressing a number of errors of the second error type in terms of a number of errors of the first error type with reference to the verification data, and by assuming relative proportions of the first and second event types within the plurality of events.

Abstract: Methods and apparatus are described for evaluating a binary classification system operable to classify each of a plurality of events as a first event type or a second event type. At least some of the events of the first event type are independently verifiable with reference to verification data. The binary classification system is susceptible to a first error type in which events of the first event type are classified as the second event type, and a second error type in which events of the second event type are classified as the first event type. Operation of a first configuration of the binary classification system is evaluated with reference to an objective function. The objective function is derived by expressing a number of errors of the second error type in terms of a number of errors of the first error type with reference to the verification data, and by assuming relative proportions of the first and second event types within the plurality of events.

Abstract: Methods and apparatus are described for evaluating a binary classification system operable to classify each of a plurality of events as a first event type or a second event type. At least some of the events of the first event type are independently verifiable with reference to verification data. The binary classification system is susceptible to a first error type in which events of the first event type are classified as the second event type, and a second error type in which events of the second event type are classified as the first event type. Operation of a first configuration of the binary classification system is evaluated with reference to an objective function. The objective function is derived by expressing a number of errors of the second error type in terms of a number of errors of the first error type with reference to the verification data, and by assuming relative proportions of the first and second event types within the plurality of events.

Abstract: Machine learning techniques are employed to build and evolve classifiers (e.g., decision trees or other rule-based classifiers) which generate scores representing confidence values associated with particular paths through a classifier (rather than discrete class labels), and then compare those scores to tunable thresholds to effect classification.

Abstract: A method of classifying documents includes: specifying multiple documents and classes, wherein each document includes a plurality of features and each document corresponds to one of the classes; determining reduced document vectors for the classes from the documents, wherein the reduced document vectors include features that satisfy threshold conditions corresponding to the classes; determining reduced weight vectors for relating the documents to the classes by comparing combinations of the reduced weight vectors and the reduced document vectors and separating the corresponding classes; and saving one or more values for the reduced weight vectors and the classes. Specific embodiments are directed to formulations for determining the reduced weight vectors including one-versus-rest classifiers, maximum entropy classifiers, and direct multiclass Support Vector Machines.

Abstract: Methods and apparatus are described for evaluating a binary classification system operable to classify each of a plurality of events as a first event type or a second event type. At least some of the events of the first event type are independently verifiable with reference to verification data. The binary classification system is susceptible to a first error type in which events of the first event type are classified as the second event type, and a second error type in which events of the second event type are classified as the first event type. Operation of a first configuration of the binary classification system is evaluated with reference to an objective function. The objective function is derived by expressing a number of errors of the second error type in terms of a number of errors of the first error type with reference to the verification data, and by assuming relative proportions of the first and second event types within the plurality of events.

Abstract: A user interface method and apparatus for an electronic device operates by detecting (108) a stroke of a touch sensitive display (170) forming a part of the electronic device. The stroke is categorized (116) as one of a swish type stroke and a non-swish type stroke. If the stroke is a non-swish stroke, it is translated (132) into an application function. If the stroke is a swish stroke, it is converted (128) to a character input function. The touch sensitive display (170) has a grid (50) containing cells (51, 53, etc.), with each cell containing a plurality of characters, and the grid can overlay application interface to thereby occupying a common area, up to and including the entire area, of the touch sensitive display (170).

Abstract: A user interface method and apparatus for an electronic device operates by detecting (108) a stroke of a touch sensitive display (170) forming a part of the electronic device. The stroke is categorized (116) as one of a swish type stroke and a non-swish type stroke. If the stroke is a non-swish stroke, it is translated (132) into an application function. If the stroke is a swish stroke, it is converted (128) to a character input function. The touch sensitive display (170) has a grid (50) containing cells (51, 53, etc.), with each cell containing a plurality of characters, and the grid can overlay application interface to thereby occupying a common area, up to and including the entire area, of the touch sensitive display (170).