Abstract: This invention provides methods and systems for classification of data instances using tree-based classifiers. Tree-based classifiers are first built according to a particular structure using a technique known as “boosting” wherein simple rules are successively added to the tree-based classifier until the unit tree-based classifier exhibits satisfactory error performance. The resulting classifier can then accurately classify an instance of data submitted to it and the classifier can optionally provide a measure of confidence for the classification result produced by the tree-based classifier.

Abstract: An Internet access system and method includes active link status indicators that give a user control over when a document is loaded into a cache. The system and method notifies a user of the presence or absence of a document in the cache by changing the visual characteristics of an associated link status indicator. If a user selects a link status indicator with a mouse or other input device, the system and method will fetch the document corresponding to the associated Internet link and place the document in the cache without displaying the document to the user. The system and method of this invention allow a user to fetch a document and store it in the cache while the user is viewing another document. The link status indicators include icons that are displayed adjacent to the associated Internet link.

Abstract: Apparatus and methods for machine learning the hypotheses used in the classifier component of pattern classification devices such as OCRs, other image analysis systems, and and text retrieval systems. The apparatus and methods employ machine learning techniques for generating weak hypotheses from a set of examples of the patterns to be recognized and then evaluate the resulting hypothesis against example patterns. The results of the evaluation are used to increase the probability that the examples used to generate the next weak hypothesis are ones which the previous weak hypothesis did not correctly classify. The results of the evaluation are also used to give a weight to each weak hypothesis. A strong hypothesis is then made by combining the weak hypotheses according to their weights.