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  • 1
    Electronic Resource
    Electronic Resource
    Toward Efficient Agnostic Learning (1994)
    Springer
    Machine learning 17 (1994), S. 115-141 
    Details
    ISSN: 0885-6125
    Keywords: machine learning ; agnostic learning ; PAC learning ; computational learning theory
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termed agnostic learning, in which we make virtually no assumptions on the target function. The name derives from the fact that as designers of learning algorithms, we give up the belief that Nature (as represented by the target function) has a simple or succinct explanation. We give a number of positive and negative results that provide an initial outline of the possibilities for agnostic learning. Our results include hardness results for the most obvious generalization of the PAC model to an agnostic setting, an efficient and general agnostic learning method based on dynamic programming, relationships between loss functions for agnostic learning, and an algorithm for a learning problem that involves hidden variables.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022615600103
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    Learning Probabilistic Read-once Formulas on Product Distributions (1994)
    Springer
    Machine learning 14 (1994), S. 47-81 
    Details
    ISSN: 0885-6125
    Keywords: computational learning theory ; PAC-learning ; learning with noise ; read-once formulas ; product distributions
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper presents a polynomial-time algorithm for inferring a probabilistic generalization of the class of read-once Boolean formulas over the usual basis { AND, OR, NOT }. The algorithm effectively infers a good approximation of the target formula when provided with random examples which are chosen according to any product distribution, i.e., any distribution in which the setting of each input bit is chosen independently of the settings of the other bits. Since the class of formulas considered includes ordinary read-once Boolean formulas, our result shows that such formulas are PAC learnable (in the sense of Valiant) against any product distribution (for instance, against the uniform distribution). Further, this class of probabilistic formulas includes read-once formulas whose behavior has been corrupted by large amounts of random noise. Such noise may affect the formula's output (“misclassification noise”), the input bits ( “attribute noise”), or it may affect the behavior of individual gates of the formula. Thus, in this setting, we show that read-once formula's can be inferred (approximately), despite large amounts of noise affecting the formula's behavior.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022646704993
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 3
    Electronic Resource
    Electronic Resource
    Learning probabilistic read-once formulas on product distributions (1994)
    Springer
    Machine learning 14 (1994), S. 47-81 
    Details
    ISSN: 0885-6125
    Keywords: computational learning theory ; PAC-learning ; learning with noise ; read-once formulas ; product distributions
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper presents a polynomial-time algorithm for inferring a probabilistic generalization of the class of read-once Boolean formulas over the usual basis {AND, OR, NOT}. The algorithm effectively infers a good approximation of the target formula when provided with random examples which are chosen according to anyproduct distribution, i.e., any distribution in which the setting of each input bit is chosen independently of the settings of the other bits. Since the class of formulas considered includes ordinary read-once Boolean formulas, our result shows that such formulas are PAC learnable (in the sense of Valiant) against any product distribution (for instance, against the uniform distribution). Further, this class of probabilistic formulas includes read-once formulas whose behavior has been corrupted by large amounts of random noise. Such noise may affect the formula's output (“misclassification noise”), the input bits (“attribute noise”), or it may affect the behavior of individual gates of the formula. Thus, in this setting, we show that read-once formula's can be inferred (approximately), despite large amounts of noise affecting the formula's behavior.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00993162
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 4
    Electronic Resource
    Electronic Resource
    Toward efficient agnostic learning (1994)
    Springer
    Machine learning 17 (1994), S. 115-141 
    Details
    ISSN: 0885-6125
    Keywords: machine learning ; agnostic learning ; PAC learning ; computational learning theory
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termedagnostic learning, in which we make virtually no assumptions on the target function. The name derives from the fact that as designers of learning algorithms, we give up the belief that Nature (as represented by the target function) has a simple or succinct explanation. We give a number of positive and negative results that provide an initial outline of the possibilities for agnostic learning. Our results include hardness results for the most obvious generalization of the PAC model to an agnostic setting, an efficient and general agnostic learning method based on dynamic programming, relationships between loss functions for agnostic learning, and an algorithm for a learning problem that involves hidden variables.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00993468
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
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