Abstract: Non-linear classifiers and dimension reduction techniques may be applied to text classification. Non-linear classifiers such as random forest, Nyström/Fisher, and others, may be used to determine criteria usable to classify text into one of a plurality of categories. Dimension reduction techniques may also be used to reduce feature space size. Machine learning techniques may be used to develop criteria (e.g., trained models) that can be used to automatically classify text. Automatic classification rates may be improved and result in fewer numbers of text samples being unclassifiable or being incorrectly classified. User-generated content may be classified, in some embodiments.

Abstract: Non-linear classifiers and dimension reduction techniques may be applied to text classification. Non-linear classifiers such as random forest, Nyström/Fisher, and others, may be used to determine criteria usable to classify text into one of a plurality of categories. Dimension reduction techniques may also be used to reduce feature space size. Machine learning techniques may be used to develop criteria (e.g., trained models) that can be used to automatically classify text. Automatic classification rates may be improved and result in fewer numbers of text samples being unclassifiable or being incorrectly classified. User-generated content may be classified, in some embodiments.

Abstract: Described herein is a multi-section housing unit for a trash bin comprising: one or three sides, a top surface and a bottom surface such that an inner space is defined within the housing unit, and wherein said inner space is separated into three compartments; said top surface having three openings, each of said openings positioned over each compartment; three cabinet doors, wherein each cabinet door defines a portion of the one to three sides of the bin and opens into one of the three said compartments; and an opening means on each cabinet door. The multi-section housing unit is useful for the collection of waste and the separation of trash, recyclable, and compostable waste.

Abstract: Systems, methods and software products analyze messages of a message stream based upon human generated concept recognizers. A sample set of messages, representative of messages from the message stream, are analyzed to determine interesting or useful categories. Text categorization engines are then trained, using the sample set and text classifiers are published. These text classifiers are then used to categorizing further text messages from the message stream.

Abstract: A concept recognition system includes a concept recognition training system and a real-time system. The concept recognition training system processes a training set and produces a lexical profile keyed to a target category. The lexical profile comprises a set of lexical cues, which are words and phrases associated with the target category. A trainer starts with an initial lexical profile that comprises a small set of seed cues. The training system retrieves samples from the training set that match lexical cues in the lexical profile. The trainer determines which of the retrieved samples are positive instances of the target category. The training system extracts lexical cues from the positive instances and adds new lexical cues to the lexical profile. The real-time system uses the lexical profile as the basis for making confidence judgments for each new incoming message from the same input stream with respect to whether the message is an instance of the target category.

Abstract: A method for maintaining the performance of the system while identifying and reclaiming unreferenced tuples. A set of status bits is associated with each tuple, which are used to indicate the current state of the tuple, and whether it is in a transient state while the garbage collector, according to this invention, is examining it, thus permitting the garbage collector to run without locking out other processing. Further, the garbage collector uses the status bits to audit linked lists used by a database manager against linkage failure. Finally, the garbage collector compresses data and removes redundant data found while traversing the tuple structure of the database.

Abstract: A shared tuple method for storing data in a database which supports modification of view relations without causing anomalous changes to logically unrelated tuples in the same or other relations. A generated key is generated by the database which uniquely identifies data in a child tuple. As a result, virtual parent relations are referenced by an original primary key, but has its underlying attribute values stored in one or more secondary relations whose access is gained through stored generated keys. The application interface is unaffected, because the system transparently pieces together the virtual relation from the primary and one or more secondary relations.