'Pattern space' presentation slideshows

Supervised Learning I: Perceptrons and LMS

Supervised Learning I: Perceptrons and LMS. Instructor: Tai-Yue (Jason) Wang Department of Industrial and Information Management Institute of Information Management. Two Fundamental Learning Paradigms. Non-associative an organism acquires the properties of a single repetitive stimulus.

1.22k views • 121 slides

Supervised Learning I: Perceptrons and LMS

Supervised Learning I: Perceptrons and LMS. Instructor: Tai-Yue (Jason) Wang Department of Industrial and Information Management Institute of Information Management. Two Fundamental Learning Paradigms. Non-associative an organism acquires the properties of a single repetitive stimulus.

1.3k views • 120 slides

Lecture 5

Lecture 5. sed and awk. Last week. Regular Expressions grep egrep. Today. Stream manipulation: sed awk. Sed: S tream-oriented, Non-Interactive, Text Ed itor. Look for patterns one line at a time, like grep Change lines of the file Non-interactive text editor

714 views • 71 slides

sed and awk

sed and awk. Last lecture. Regular Expressions grep egrep. Today. Sed Awk. Sed: S tream-oriented, Non-Interactive, Text Ed itor. Look for patterns one line at a time, like grep Change lines of the file Non-interactive text editor Editing commands come in as script

687 views • 67 slides

Phantom Limb Phenomena

Phantom Limb Phenomena. Hand movement observation by individuals born without hands: phantom limb experience constrains visual limb perception. Funk M , Shiffrar M , Brugger P.

590 views • 55 slides

549 views • 54 slides

Unsupervised Learning

Unsupervised Learning. Neural Networks 11. Unsupervised learning: simple competitive learning Biological background: Neurons are wired topographically, nearby neurons connect to nearby neurons. In visual cortex, neurons are organized in functional columns.

404 views • 40 slides

Pattern Recognition: Statistical and Neural

Nanjing University of Science & Technology. Pattern Recognition: Statistical and Neural. Lonnie C. Ludeman Lecture 26 Nov 4, 2005. Lecture 26 Topics. General Concept of Clustering Basic problems in determining clusters Definition of distance functions between clusters

404 views • 39 slides

Stream-Oriented, Non-Interactive EDitor sed

420 views • 34 slides

Sorin Alexe RUTCOR, Rutgers University, Piscataway, NJ e-mail: salexe@rutcor.rutgers

DIMACS Mixer Series, September 19, 2002. Datascope - a new tool for Logical Analysis of Data (LAD). Sorin Alexe RUTCOR, Rutgers University, Piscataway, NJ e-mail: salexe@rutcor.rutgers.edu URL: rutcor.rutgers.edu/~salexe. Hidden Function. LAD Approximation. LAD - Problem.

414 views • 31 slides

Parameter Tuning for Differential Mining of String Patterns

Parameter Tuning for Differential Mining of String Patterns. J.Besson, C. Rigotti , I. Mitasiunaite and J.-F. Boulicaut. Tuning extraction parameters. Local pattern mining: itemsets, closed itemsets, episodes, seq. patterns, substrings

363 views • 26 slides

Unix Talk #2

347 views • 25 slides

Unix Talk #2

331 views • 25 slides

Unix Talk #2

258 views • 25 slides

Chapter 13: sed

300 views • 19 slides