G. James and T. Hastie. The error coding method and PiCTs. Journal of Computational and Graphical Statistics, 7:3:377--387, 1997. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
On the Learnability and Design of Output Codes for Multiclass .. - Crammer, Singer (2000) (23 citations) (Correct)
.... Bakiri, 1995; Dietterich Kong, 1995; Kong Dietterich, 1995; Aha Bankert, 1997; Schapire, 1997; Dietterich, 1999; Berger, 1999; Allwein, Schapire, Singer, 2000) The performance of output coding was also analyzed in statistics and learning theoretic contexts (Hastie Tibshirani, 1998; James Hastie, 1998; Schapire Singer, 1999; Allwein et al. 2000) Most of the previous work on output coding has concentrated on the problem of solving multiclass problems using predefined output codes, independently of the specific application and the class of hypotheses used to construct the binary classifiers. ....
James, G., & Hastie, T. (1998). The error coding method and PiCT. Journal of computational and graphical stastistics, 7(3), 377--387.
Error-Correcting Output Coding for Text Classification - Berger (1999) (19 citations) (Correct)
....at once. Moreover, assuming n is sufficiently large) an ECOC classifier learns each boundary many times, and is forgiving if a few PiCs place the input x on the wrong side of some decision boundaries [ Kong and Dietterich, 1995 ] 3 Choosing a good code Empirical work has established [ James and Hastie, 1997 ] that ECOC classification performs well when the coding matrix C is constructed randomly specifically, by choosing each entry C ij uniformly at random from f0; 1g. The next two sections provides some statistical and combinatorial arguments for why this should be so. 3.1 A statistical ....
G. James and T. Hastie. The error coding method and picts. Journal of Computational and Graphical Statistics, 7:3:377--387, 1997.
Information Retrieval and Information Theory - Berger (1999) (Correct)
....smallnumber of decision boundaries at once. Moreover, assumingn is sufficiently large) an ECOC classifier learns each boundary many times, and is forgiving if a few PiCs place the input x on the wrong side of some decision boundaries [24] 3. 2 Choosing a good code Empirical work has established [22] that ECOC classification performs well when the coding matrix C is constructed randomly specifically, by choosing each entry C ij uniformly at random from f0; 1g. The next two sections provides some statistical and combinatorial arguments for why this should be so. 3.2.1 A statistical ....
G. James and T. Hastie. The error coding method and PiCTs. Journal of Computational and Graphical Statistics, 7:3:377--387, 1997.
Error-Correcting Output Coding for Text Classification - Berger (1999) (19 citations) (Correct)
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G. James and T. Hastie. The error coding method and PiCTs. Journal of Computational and Graphical Statistics, 7:3:377--387, 1997.
Error-Correcting Output Coding for Text Classification - Berger (1999) (19 citations) (Correct)
No context found.
G. James and T. Hastie. The error coding method and PiCTs. Journal of Computational and Graphical Statistics, 7:3:377--387, 1997.
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