'Machine learning methods' presentation slideshows

Document-level Semantic Orientation and Argumentation

Document-level Semantic Orientation and Argumentation. Presented by Marta Tatu CS7301 March 15, 2005. ? or ?? Semantic Orientation Applied to Unsupervised Classification of Reviews. Peter D. Turney ACL-2002. Overview.

464 views • 46 slides

Information Retrieval

Information Retrieval. Lecture 5. Recap of last time. Postings pointer storage Dictionary storage Compression Wild-card queries. This lecture. Query expansion What queries can we now process? Index construction Estimation of resources. Spell correction and expansion. Expansion.

534 views • 45 slides

Machine Learning in Bioinformatics

Machine Learning in Bioinformatics. Simon Colton The Computational Bioinformatics Laboratory. Talk Overview. Our research group Aims, people, publications Machine learning A balancing act Bioinformatics Holy grails Our bioinformatics research projects From small to large

520 views • 35 slides

Functional characterization of membrane transporters from protein sequences

Functional characterization of membrane transporters from protein sequences. Haiquan Li The Samuel Roberts Noble Foundation. Membrane transport proteins (transporters). Functions Uptake of nutrients (nitrogen) Pump out toxic metabolites Mediate signal transduction

544 views • 30 slides

REFERENTIAL CHOICE AS A PROBABILISTIC MULTI-FACTORIAL PROCESS

REFERENTIAL CHOICE AS A PROBABILISTIC MULTI-FACTORIAL PROCESS. Andrej A. Kibrik , Grigorij B. Dobrov , Natalia V. Loukachevitch , Dmitrij A. Zalmanov aakibrik@gmail.com. Referential choice in discourse.

404 views • 29 slides

Learning from observations

294 views • 28 slides

Chuck! Parts 3 and 4: PLOrk and machine learning

Chuck! Parts 3 and 4: PLOrk and machine learning. Rebecca Fiebrink Princeton University. Part 3: The Princeton Laptop Orchestra. 4 years old, first of its kind Now laptop orchestras in Stanford U., Tokyo, London, Oslo, and many others 15+ students

377 views • 27 slides

ASQA: A cademia S inica Q uestion A nswering System for CLQA (IASL)

ASQA: A cademia S inica Q uestion A nswering System for CLQA (IASL). Cheng-Wei Lee , Cheng-Wei Shih, Min-Yuh Day, Tzong-Han Tsai, Tian-Jian Jiang, Chia-Wei Wu, Cheng-Lung Sung, Yu-Ren Chen, Shih-Hung Wu , Wen-Lian Hsu Academia Sinica, Taipei aska@iis.sinica.edu.tw. Outline.

380 views • 21 slides

CS 445/545 Machine Learning Spring, 2013

274 views • 18 slides

The Next Generation of AI and Deep Learning - GTC17

Meet our featured AI and Deep Learning experts from Amazon, Microsoft, Stanford and more, speaking at #GTC17 this year.

272 views • 15 slides

David John Gagne II

Integration of Storm Scale Ensembles, Hail Observations, and Machine Learning for Severe Hail Prediction. David John Gagne II Center for Analysis and Prediction of Storms (CAPS)/ School of Meteorology, University of Oklahoma RAL, NCAR, Boulder, CO Jerry Brotzge

306 views • 13 slides

Defining Machine Learning

245 views • 13 slides

A Statistical Approach to Star Rating Classification of Sentiment

A Statistical Approach to Star Rating Classification of Sentiment. Alexander Hogenboom Erasmus University Rotterdam hogenboom@ese.eur.nl. Introduction (1). The Web offers an overwhelming amount of textual data, containing traces of sentiment

280 views • 11 slides