'Feature vectors' diaporamas de présentation

Adam Johnson Department of Psychology

The Bayesian Brain: Structure learning and information foraging. Adam Johnson Department of Psychology. Learning. Classical conditioning. Intertrial interval (ITI). CS. UCS. Learning. Classical conditioning Acquisition, extinction, re-acquisition. Extinction and learning

1.13k views • 103 slides

Speech Recognition

Speech Recognition. Components of a Recognition System. Frontend. Feature extractor. Frontend. Feature extractor Mel-Frequency Cepstral Coefficients (MFCCs). Feature vectors. Hidden Markov Models ( HMMs ). Acoustic Observations. Hidden Markov Models ( HMMs ). Acoustic Observations

1.45k views • 69 slides

Similarity Searches in Sequence Databases

Similarity Searches in Sequence Databases. Sang-Hyun Park KMeD Research Group Computer Science Department University of California, Los Angeles. Contents. Introduction Whole Sequence Searches Subsequence Searches Segment-Based Subsequence Searches Multi-Dimensional Subsequence Searches

858 views • 67 slides

Learning I

Learning I. Linda Shapiro CSE 455 . Learning. AI/Vision systems are complex and may have many parameters. It is impractical and often impossible to encode all the knowledge a system needs. Different types of data may require very different parameters.

899 views • 62 slides

870 views • 56 slides

Syllables and Concepts in Large Vocabulary Continuous Speech Recognition

Syllables and Concepts in Large Vocabulary Continuous Speech Recognition. Paul De Palma Ph. D. Candidate Department of Linguistics University of New Mexico Slides available at: www.cs.gonzaga.edu/depalma. An Engineered Artifact. Syllables Principled word segmentation scheme

632 views • 48 slides

TAILS: COBWEB 1 [1]

TAILS: COBWEB 1 [1]. Online Digital Learning Environment for Conceptual Clustering.

574 views • 37 slides

Friends and Locations Recommendation with the use of LBSN

Friends and Locations Recommendation with the use of LBSN. By EKUNDAYO OLUFEMI ADEOLA 27.02.2014. Outline. Understanding Users Users routine activities minning Semantic location history User similarities calculation Travel Recommendations Location minning Detecting travel sequence

281 views • 28 slides

MediaScope : Selective On-Demand Media Retrieval from Mobile Devices

MediaScope : Selective On-Demand Media Retrieval from Mobile Devices. Yurong Jiang, Xing Xu , Peter Terlecky , Tarek Abdelzaher , Amotz Bar- Noy , Ramesh Govindan. IPSN 2013. Availability Gap. Availability Gap in. Bridge the Availability Gap ?. Bridge the Availability Gap.

383 views • 26 slides

Feature Based Similarity

410 views • 23 slides

Speaker Recognition

Speaker Recognition. Searching & Decoding in SR ECE5527 Wilson Burgos. Outline. Introduction Objective Implementation Simulation Test and Result Conclusion. Introduction. Speaker Recognition aims to recognize speakers from their voices Divided into identification and verification.

694 views • 22 slides

A. M. Sharaf, Senior Member, IEEE University of New Brunswick, Fredericton, NB, Canada

A New Current Pattern Recognizing Protection Technique for High Impedance Fault Using Cross Correlation. A. M. Sharaf, Senior Member, IEEE University of New Brunswick, Fredericton, NB, Canada. M. M. Eissa, Senior Member, IEEE Faculty of Engineering, Helwan University, EGYPT.

373 views • 22 slides

Investigation on Inter-Speaker Variability in The Feature Space

237 views • 15 slides

Machine Learning

Machine Learning. Lecture 6 K-Nearest Neighbor Classifier. x 1. 2. 3. 1. 4. 5. 6. 7. 10. 9. 11. 12. 13. 14. 8. x 2. 15. 16. Elliptical blobs (objects). Objects, Feature Vectors, Points. X(15). X(1). X(7). X(16). X(3). X(8). X(25). X(12). X(13). X(6). X(9). X(10).

265 views • 14 slides

FUZZY CLUSTERING OF BRAND PRODUCT CUSTOMER LOYALTY DATA

FUZZY CLUSTERING OF BRAND PRODUCT CUSTOMER LOYALTY DATA. PhD Serhiy Shtovba and Olena Shtovba Vinnitsa National Technical University, Khmelnitskoe Shosse, 95, Vinnitsa, 21021, UKRAINE shtovba@ksu.vstu.vinnica.ua www.vinnitsa.com/shtovba.

253 views • 14 slides

GraphSig : Mining Significant Substructures in Compound Libraries

GraphSig : Mining Significant Substructures in Compound Libraries. GraphSig. Provides powerful insight into structure-activity relationship in the form of significant substructures . Input: Diverse background database Libraries of compounds with specific activity Output:

279 views • 13 slides

Voiceprint System Development Design, implement, test unique voiceprint biometric system

Voiceprint System Development Design, implement, test unique voiceprint biometric system. Research Day Presentation, May 3 rd 2013 Rahul Raj ( Team Lead), Geeta Bothe, Mahesh Sooryambylu , Ravi Ray, Sreeram Vancheeswaran IBM India Customer: Jonathan Leet (DPS 2013)

303 views • 12 slides

Dynamic Time Warping Applications and Derivation

Dynamic Time Warping Applications and Derivation. Charles Tappert Seidenberg School of CSIS, Pace University. Dynamic Time Warping (DTW) non-linear/elastic matching, Viterbi algorithm. Many Applications Speech recognition Speech sound alignment Speech sound generation

311 views • 12 slides

Learning How to Play Black Jack Through Reinforcement Learning

Learning How to Play Black Jack Through Reinforcement Learning. By: Jonathan Quenzer. Objectives. To have a computer learn how to play Blackjack through reinforcement learning Computer starts off with no memory. After each hand is played, the computer learns more.

224 views • 8 slides

S emantic S imilarity for M usic R etrieval

A coustic M odels. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +. +.

84 views • 1 slides