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Enterprise content repositories often consist of business documents comprising not only of traditional text data but also graphics (org charts, graphs, architecture diagrams, etc.) that get reused by people across the enterprise. Despite this diversity of content, most of the research in enterprise search has focused on improving document search. We describe a machine learning approach for graphics...
Conference web pages display their topics information in different ways, and conferences in different domains accept papers on different topics. Automatic extraction of topics information from conference web pages is thus a difficult task and has not received much attention from the research community. In this paper, we propose a method for extracting topics information that uses a web page segmentation...
In this paper we introduce and exploit the concept of contextual rules in the field of object detection. These rules are defined as associations between different object likelihood maps and are learned from given examples. The contextual rules can be used to prime regions where a target object category occurs in an image given areas of other object categories. The principal idea is to locate several...
We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities. It is based on the decomposition of a model's predictions on individual contributions of each attribute. Our method works for the so-called black box models such as support vector machines, neural networks, and nearest...
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