2 papers available

[thrun+@HEAVEN.LEARNING.CS.CMU.EDU]

Dear Colleagues:
I am happy to announce two new papers:





		   Lifelong Learning: A Case Study

			   Sebastian Thrun

Machine learning has not yet succeeded in the design of robust
learning algorithms that generalize well from very small datasets.  In
contrast, humans often generalize correctly from only a single
training example, even if the number of potentially relevant features
is large. To do so, they successfully exploit knowledge acquired in
previous learning tasks, to bias subsequent learning.

This paper investigates learning in a lifelong context.  Lifelong
learning addresses situations where a learner faces a stream of
learning tasks.  Such scenarios provide the opportunity for synergetic
effects that arise if knowledge is transferred across multiple
learning tasks.  To study the utility of transfer, several approaches
to lifelong learning are proposed and evaluated in an object
recognition domain. It is shown that all these algorithms generalize
consistently more accurately from scarce training data than comparable
"single-task" approaches.


World Wide Web URL: 
	http://www.cs.cmu.edu/~thrun/papers/thrun.lll_case_study.ps.Z 



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	     Clustering Learning Tasks and the Selective
		   Cross-Task Transfer of Knowledge

		Sebastian Thrun and Joseph O'Sullivan


Recently, there has been an increased interest in machine learning
methods that learn from more than one learning task.  Such methods
have repeatedly found to outperform conventional, single-task learning
algorithms when learning tasks are appropriately related. To increase
robustness of these approaches, methods are desirable that can reason
about the relatedness of individual learning tasks, in order to avoid
the danger arising from tasks that are unrelated and thus potentially
misleading.

This paper describes the task-clustering (TC) algorithm. TC clusters
learning tasks into classes of mutually related tasks. When facing a
new thing to learn, TC first determines the most related task cluster,
then exploits information selectively from this task cluster only.  An
empirical study carried out in a mobile robot domain shows that TC
outperforms its unselective counterpart in situations where only a
small number of tasks is relevant.

World Wide Web URL:
	http://www.cs.cmu.edu/~thrun/papers/thrun.TC.ps.Z





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INSTRUCTIONS FOR RETRIEVAL:

(a) If you have access to the World Wide Web, you can retrieve the
    documents from my homepage (URL: http://www.cs.cmu.edu/~thrun,
    follow the paper link) or access them directly:

      netscape http://www.cs.cmu.edu/~thrun/papers/thrun.lll_case_study.ps.Z
      netscape http://www.cs.cmu.edu/~thrun/papers/thrun.TC.ps.Z


(b) If you instead wish to retrieve the documents via anonymous ftp,
    follow these instructions:

      unix>	ftp uran.informatik.uni-bonn.de
      user:	anonymous		
      passwd:	aaa at bbb.ccc
      ftp>	cd pub/user/thrun
      ftp>	bin
      ftp>	get thrun.lll_case_study.ps.Z 
      ftp>	get thrun.TC.ps.Z
      ftp>	bye
      unix>	uncompress thrun.lll_case_study.ps.Z
 		unix>	uncompress thrun.TC.ps.Z
      unix>	lpr thrun.lll_case_study.ps.Z
 		unix>	lpr thrun.TC.ps.Z


(c) Hard-copies can be obtained directly from

      Technical Reports
      Computer Science Department
      Carnegie Mellon University
      5000 Forbes Ave
      Pittsburgh, PA 15213
      Email: reports at cs.cmu.edu

    Please refer to the first paper as TR CMU-CS-95-208
    and the second paper as TR CMU-CS-95-209




Comments are welcome!
Sebastian Thrun