Document relevancy analysis within machine learning systems including determining closest cosine distances of training examples

US8533148B1 · kind B1 · utility

Assignee

• Recommind, Inc. · US

Inventors

• Christian Feuersänger · Rheinbach, DE
• Dietrich Wettschereck · Bonn, DE
• Jan Puzicha · Bonn, DE

Key dates

• Filing date: Oct 1, 2012
• Grant date: Sep 10, 2013
• Expiry date: Oct 1, 2032

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/10
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Systems and methods that quantify document relevance for a document relative to a training corpus and select a best match or best matches are provided herein. Methods may include generating an example-based explanation for relevancy of a document to a training corpus by executing a support vector machine classifier, the support vector machine classifier performing a centroid classification of a relevant document in a term frequency-inverse document frequency features space relative to training examples in a training corpus, and generating an example-based explanation by selecting a best match for the relevant document from the training examples based upon the centroid classification. Determining the training example having the closest cosine distance to the relevant document includes ranking the training examples by stretching the internal best match scores for the training examples linearly to cover a complete unit interval.