000 03969nam a22005295i 4500
001 978-3-031-01575-5
003 DE-He213
005 20240730163548.0
007 cr nn 008mamaa
008 221028s2017 sz | s |||| 0|eng d
020 _a9783031015755
_9978-3-031-01575-5
024 7 _a10.1007/978-3-031-01575-5
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aChaudhri, Zhiyuan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979226
245 1 0 _aLifelong Machine Learning
_h[electronic resource] /
_cby Zhiyuan Chaudhri, Bing Liu.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aIV, 145 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
505 0 _aPreface -- Acknowledgments -- Introduction -- Related Learning Paradigms -- Lifelong Supervised Learning -- Lifelong Unsupervised Learning -- Lifelong Semi-supervised Learning for Information Extraction -- Lifelong Reinforcement Learning -- Conclusion and Future Directions -- Bibliography -- Authors' Biographies.
520 _aLifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong learning aims to achieve this capability. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to new heights. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_979227
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aLiu, Bing.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979228
710 2 _aSpringerLink (Online service)
_979229
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031004476
776 0 8 _iPrinted edition:
_z9783031027031
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_979230
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01575-5
912 _aZDB-2-SXSC
942 _cEBK
999 _c84741
_d84741