Apache Mahout

More documents

Recommendations

Info

Isabel Drost Nighttime: Came to nutch in 2004. Co-Founder <strong>Apache</strong> <strong>Mahout</strong>. Organizer of Berlin Hadoop Get Together. Daytime: Software developer @ Berlin
Hello DAI!
Page 1: Apache Mahout Large Scale Machine L
Page 5 and 6: January 3, 2006 by Matt Callow http
Page 7 and 8: September 21, 2008, Rodrigo Galinde
Page 9 and 10: Machine learning - what's that?
Page 11 and 12: Archimedes model of nature
Page 14 and 15: An SVM's model of nature
Page 16 and 17: ● Industry ready. - Friendly lice
Page 18 and 19: January 8, 2008 by Pink Sherbet Pho
Page 20 and 21: Developers world wide
Page 22 and 23: Developers world wide Developers in
Page 24 and 25: Developers world wide Developers in
Page 26 and 27: Typical developer September 10, 200
Page 28 and 29: Requirements ● Easy administratio
Page 30: Java based implementation. Easy dis
Page 33 and 34: Contributions need not be Hadoop ba
Page 35 and 36: pattern=”http://[0-9A-Za-z\-_\.]*
Page 37 and 38: Assumptions: Data to process does n
Page 39 and 40: M A P | SHUFFLE | R E D U C E Local
Page 41 and 42: M A P | SHUFFLE | R E D U C E outpu
Page 43 and 44: private IntWritable one = new IntWr
Page 45 and 46: Options for running Hadoop ● Amaz
Page 47 and 48: Step 1: Serial implementation.
Page 49 and 50: Step 2: Parallelization strategies.
Page 51 and 52: Step 2: Parallelization strategies.
Page 53 and 54:
Algorithm properties ● Job local
Page 55 and 56:
Discover groups of items ● Group
Page 57:
Going parallel: k-Means
Page 63 and 64:
Until stable. Data intensive. Outpu
Page 65 and 66:
Assign items to defined categories.
Page 67 and 68:
Evolutionary algorithms ● Traveli
Page 69 and 70:
Recommendation mining. ● ● ●
Page 71 and 72:
What next? ● More algorithms. ●
Page 73 and 74:
Reasons: Promotion, recruiting, and
Page 75 and 76:
June 13, 2008, Star Guitar http://w
Page 77 and 78:
Ted Dunning: “[...] if I have a c
Page 79 and 80:
Jumpstart your project with proven
Page 81 and 82:
Become part of the community.
Page 83 and 84:
Sept., 29 th 2009: Hadoop* Get Toge
Page 87 and 88:
Open questions. Gather data Large s
Page 89 and 90:
Algor. choice Gather data Extract f
Page 91 and 92:
Algor. choice Parameters Train mode
Page 93:
Algor. choice Parameters Train mode
show all

Apache Mahout

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?