Abstract: Methods, apparatus, and machine-readable mediums are described for selecting a training set from a larger data set. Samples are divided into a training set and a validation set. Each set meets one or more conditions. For each class to be modeled, multiple training sets are created. Models are trained on each of the multiple training sets. A size of samples for each class is determined based upon the trained models. A training data set that includes a number of samples based upon the determined size of samples is created.

Abstract: Methods, apparatus, and machine-readable mediums are described for selecting a training set from a larger data set. Samples are divided into a training set and a validation set. Each set meets one or more conditions. For each class to be modeled, multiple training sets are created. Models are trained on each of the multiple training sets. A size of samples for each class is determined based upon the trained models. A training data set that includes a number of samples based upon the determined size of samples is created.

Abstract: Methods and apparatus are described by which a reduced training data set is created from an initial training data set. Examples within the initial training data set are classified into various classes, such as noise and boundary classes, based upon other examples within the vicinity of the example. Examples are then filtered from the initial training data set based upon the classification and properties of the examples within the vicinity of an example. The filtered training data set is used to create a classifier model. The classifier models are less complex and require less computation resources compared to a model created with the entire initial training data set.

Abstract: System and techniques for an embedded sensor model are described herein. A message that includes a model identifier field, a performance label, a sensor set, and a first user identification field containing a user identification is obtained. A set of feedback packages is obtained. A feedback package includes a value and indicates the user identification. Feedback package values are aggregated to create a weight for the user identification. A training set is applied to a model to create a new model. This includes modifying model training with respect to the sensor set based on the performance label and the weight. The new model may then be transmitted to a user device. The new model providing a sensor classifier for a sensor monitoring user activity.