<--- Back to Details
First PageDocument Content
Linear algebra / Machine learning / Biometrics / Surveillance / Pattern recognition / Keystroke dynamics / Vector space / Euclidean vector / X Window System / Algebra / Mathematics / Abstract algebra
Date: 2010-07-24 14:01:23
Linear algebra
Machine learning
Biometrics
Surveillance
Pattern recognition
Keystroke dynamics
Vector space
Euclidean vector
X Window System
Algebra
Mathematics
Abstract algebra

A Behavioural Biometric System Based on Human Computer Interaction Hugo Gamboaa and Ana Fredb a Escola Superior de Tecnologia de Set´

Add to Reading List

Source URL: www.lx.it.pt

Download Document from Source Website

File Size: 286,09 KB

Share Document on Facebook

Similar Documents

A COMPLETE WORST-CASE ANALYSIS OF KANNAN’S SHORTEST LATTICE VECTOR ALGORITHM ´† GUILLAUME HANROT∗ AND DAMIEN STEHLE Abstract. Computing a shortest nonzero vector of a given euclidean lattice and computing a closes

A COMPLETE WORST-CASE ANALYSIS OF KANNAN’S SHORTEST LATTICE VECTOR ALGORITHM ´† GUILLAUME HANROT∗ AND DAMIEN STEHLE Abstract. Computing a shortest nonzero vector of a given euclidean lattice and computing a closes

DocID: 1uAnv - View Document

The Kepler Problem Revisited: The Runge–Lenz Vector Math 241 Homework John Baez  Whenever we have two particles interacting by a central force in 3d Euclidean space, we have

The Kepler Problem Revisited: The Runge–Lenz Vector Math 241 Homework John Baez Whenever we have two particles interacting by a central force in 3d Euclidean space, we have

DocID: 1shYd - View Document

The Kepler Problem Revisited: The Laplace–Runge–Lenz Vector March 6, 2008 John Baez  Whenever we have two particles interacting by a central force in 3d Euclidean space, we have

The Kepler Problem Revisited: The Laplace–Runge–Lenz Vector March 6, 2008 John Baez Whenever we have two particles interacting by a central force in 3d Euclidean space, we have

DocID: 1sei8 - View Document

– the FoM for “rock” appears to have become very poor now. • Combining all feature dimensions from acoustic information below 20 Hz and above 4186 Hz. – (Rock recovers partly) 3.3MUSIC FilteringCONTENT

– the FoM for “rock” appears to have become very poor now. • Combining all feature dimensions from acoustic information below 20 Hz and above 4186 Hz. – (Rock recovers partly) 3.3MUSIC FilteringCONTENT

DocID: 1rtLo - View Document

The Intervalgram: An Audio Feature for Large-scale Melody Recognition Thomas C. Walters, David A. Ross, and Richard F. Lyon Google, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA

The Intervalgram: An Audio Feature for Large-scale Melody Recognition Thomas C. Walters, David A. Ross, and Richard F. Lyon Google, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA

DocID: 1rsKd - View Document