M
Michelangelo Diligenti
Researcher at University of Siena
Publications - 79
Citations - 2107
Michelangelo Diligenti is an academic researcher from University of Siena. The author has contributed to research in topics: Deep learning & Inference. The author has an hindex of 19, co-authored 76 publications receiving 1883 citations. Previous affiliations of Michelangelo Diligenti include Google.
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Proceedings Article
Focused Crawling Using Context Graphs
TL;DR: A focused crawling algorithm is presented that builds a model for the context within which topically relevant pages occur on the web that can capture typical link hierarchies within which valuable pages occur, as well as model content on documents that frequently cooccur with relevant pages.
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Semantic-based regularization for learning and inference
TL;DR: A unified approach to learning from constraints is proposed, which integrates the ability of classical machine learning techniques to learn from continuous feature-based representations with the ability to reasoning using higher-level semantic knowledge typical of Statistical Relational Learning.
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Recognition of common areas in a Web page using visual information: a possible application in a page classification
TL;DR: A new, hierarchical representation that includes browser screen coordinates for every HTML object in a page is proposed that shows that a Naive Bayes classifier clearly outperforms the same classifier using only information about the content of documents.
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Hidden tree Markov models for document image classification
TL;DR: A structured representation of images based on labeled XY-trees is obtained and a probabilistic architecture that extends hidden Markov models for learning probability distributions defined on spaces of labeled trees is proposed.
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A unified probabilistic framework for Web page scoring systems
TL;DR: This work proposes a general probabilistic framework for Web page scoring systems (WPSS), which incorporates and extends many of the relevant models proposed in the literature and introduces scoring systems for both generic (horizontal) and focused (vertical) search engines.