2010 - ACM Fellow For contributions to machine-learning models of natural language and biological sequences.
Fernando Pereira focuses on Artificial intelligence, Natural language processing, Machine learning, Programming language and Parsing. The study of Artificial intelligence is intertwined with the study of Pattern recognition in a number of ways. His Natural language processing research is multidisciplinary, relying on both Speech recognition and Vector space model.
His Machine learning research is multidisciplinary, incorporating perspectives in Domain, Domain adaptation, Field and Test data. Fernando Pereira combines subjects such as Probabilistic logic and Sequence labeling with his study of Conditional random field. His Sequence labeling research includes themes of Structured prediction, Conditional entropy and Bayesian network.
Fernando Pereira spends much of his time researching Artificial intelligence, Natural language processing, Machine learning, Programming language and Natural language. His Artificial intelligence research includes elements of Speech recognition and Pattern recognition. His Pattern recognition research focuses on Discriminative model and Conditional random field.
His Natural language processing research is multidisciplinary, incorporating elements of Semantics, Bigram and Grammar. His work investigates the relationship between Bigram and topics such as Probabilistic logic that intersect with problems in Algorithm. Fernando Pereira studies Machine learning, namely Semi-supervised learning.
Fernando Pereira focuses on Petri net, Artificial intelligence, Embedded system, Programming language and Code generation. The Petri net study combines topics in areas such as User interface, Software and Web application. His Artificial intelligence research incorporates themes from Machine learning, Pattern recognition and Natural language processing.
His Machine learning study incorporates themes from Classifier, Pose and Hidden Markov model. His study looks at the relationship between Programming language and fields such as XML, as well as how they intersect with chemical problems. His Algorithm study integrates concerns from other disciplines, such as Graphical model, Conditional random field, Markov model and Maximum-entropy Markov model.
Fernando Pereira spends much of his time researching Artificial intelligence, Programming language, Natural language processing, Petri net and Information retrieval. Fernando Pereira has researched Artificial intelligence in several fields, including Machine learning, Isolation, Speech recognition and Pattern recognition. His work in the fields of Graphical model overlaps with other areas such as Set.
In the subject of general Programming language, his work in Bottom-up parsing and Top-down parsing is often linked to Sling and Frame, thereby combining diverse domains of study. His study in Rule of inference extends to Natural language processing with its themes. His biological study spans a wide range of topics, including Microcontroller, Embedded system, User interface and Control theory.
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Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
John D. Lafferty;Andrew McCallum;Fernando C. N. Pereira.
international conference on machine learning (2001)
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
John Blitzer;Mark Dredze;Fernando Pereira.
meeting of the association for computational linguistics (2007)
A theory of learning from different domains
Shai Ben-David;John Blitzer;Koby Crammer;Alex Kulesza.
Machine Learning (2010)
The information bottleneck method
Naftali Tishby;Fernando C. N. Pereira;William Bialek.
Proc. 37th Annual Allerton Conference on Communications, Control and Computing, 1999 (2000)
Maximum Entropy Markov Models for Information Extraction and Segmentation
Andrew McCallum;Dayne Freitag;Fernando C. N. Pereira.
international conference on machine learning (2000)
Shallow parsing with conditional random fields
Fei Sha;Fernando Pereira.
north american chapter of the association for computational linguistics (2003)
Domain Adaptation with Structural Correspondence Learning
John Blitzer;Ryan McDonald;Fernando Pereira.
empirical methods in natural language processing (2006)
The Unreasonable Effectiveness of Data
A. Halevy;P. Norvig;F. Pereira.
IEEE Intelligent Systems (2009)
Definite clause grammars for language analysis—A survey of the formalism and a comparison with augmented transition networks
Fernando C.N. Pereira;David H.D. Warren.
Artificial Intelligence (1980)
Analysis of Representations for Domain Adaptation
Shai Ben-David;John Blitzer;Koby Crammer;Fernando Pereira.
neural information processing systems (2006)
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