Breiman Random Forests Bibtex Bibliography

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BibTeX entries in bib/statisti.bib

@ARTICLE{battiti94, author = {Roberto Battiti}, title = {Using the mutual information for selecting features in supervised neural net learning}, journal = {IEEE Transactions on Neural Networks}, volume = {5}, number = {4}, pages = {537--550}, year = {1994}, url = {}, } @BOOK{cart84, author = {L. Breiman and J. Friedman and R. Olshen and C. Stone}, title = {{Classification and Regression Trees}}, publisher = {Wadsworth and Brooks}, address = {Monterey, CA}, year = {1984}, note = {new edition \cite{cart93}?}, remarks = {cited in \cite{cslu:esca98mm, cslu:icslp98cronk, cstr:unitsel97} for CART, clustering, and decision trees}, abstract = {}, } @BOOK{cart84-2, author = {Leo {Breiman} and J. H. {Friedman} and R. A. {Olshen} and C. J. {Stone}}, title = {Classification and Regression Trees}, year = {1984}, publisher = {Wadsworth Publishing Company}, address = {Belmont, California, U.S.A.}, series = {Statistics/Probability Series}, isbn-hard = {0534980546 (softcover)}, isbn-soft = {0534980538 (hardcover)}, } @BOOK{cart93, author = {Leo Breiman and others}, title = {{Classification and Regression Trees}}, publisher = {Chapman \& Hall}, address = {New York}, year = {1984}, pages = {358}, note = {new edition of \cite{cart84}?}, isbn = {0-412-04841-8}, url = {}, amazon-url = {}, price = {\$44.95, DM 83.26 EUR 42.57}, remarks = {\tbf}, abstract = {}, } @ARTICLE{dubnov95, author = {Shlomo Dubnov and Naftali Tishby and Dalia Cohen}, title = {{Hearing Beyond the Spectrum}}, journal = {Journal of New Music Research}, volume = {24}, publisher = {}, number = {4}, note = {}, pub-url = {}, remarks = {features: harmonicity, phase coherence, chorus. bispectral information. acoustic distortion (distance) measure (``concept of statistical divergence which is used for measuring the `similarity' between signals'', ``similarity classes with a good correspondence to the human acoustic perception'', ``generalization of acoustic distortion measure''). \tbf}, abstract = {In this work we focus on the problem of acoustic signals modeling and analysis, with particular interest in models that can capture the timbre of musical sounds. Traditional methods usually relate to several ``dimensions'' which represent the spectral properties of the signal and their change in time. Here we confine ourselves to the stationary portion of the sound signal, the analysis of which is generalized by incorporating polyspectral techniques. We suggest that by looking at the higher order statistics of the signal we obtain additional information not present in the standard autocorrelation or its Fourier related power-spectra. It is shown that over the bispectral plane several acoustically meaningful measures could be devised, which are sensitive to properties such as harmonicity and phase coherence among the harmonics. Effects such as reverberation and chorusing are demonstrated to be clearly detected by the above measures. In the second part of the paper we perform an information theoretic analysis of the spectral and bispectral planes. We introduce the concept of statistical divergence which is used for measuring the ``similarity'' between signals. A comparative matrix is presented which shows the similarity measure between several instruments based on spectral and bispectral information. The instruments group into similarity classes with a good correspondence to the human acoustic perception. The last part of the paper is devoted to acoustical modelling of the above phenomena. We suggest a simple model which accounts for some of the polyspectral aspects of musical sound discussed above. One of the main results of our work is generalization of acoustic distortion measure based on our model and which takes into account higher order statistical properties of the signal.}, } @INPROCEEDINGS{dubnov97, author = {Shlomo Dubnov and Xavier Rodet}, title = {{Statistical Modeling of Sound Aperiodicities}}, booktitle = {Proceedings of the International Computer Music Conference (ICMC)}, month = {September}, year = {1997}, address = {Tessaloniki, Greece}, note = {}, url = {}, } @PHDTHESIS{rochebois97, author = {Thierry Rochebois}, title = {{Méthodes d'analyse synthèse et représentations optimales des sons musicaux basées sur la réduction de données spectrales}}, month = {December}, year = {1997}, school = {Université Paris XI}, url = {}, remarks = {Principal components analysis of harmonic partials, gives sub-spaces as linear combinations of partials, i.e. timbral components.}, abstract = {principalL'analyse et la synthèse de sons et en particulier de sons musicaux a déjà fait l'objet de nombreuses recherches. Pour l'essentiel, ces recherches ont été menées dans deux objectifs : étudier et synthétiser les sons musicaux. Ces deux objectifs sont tout à fait conciliables et complémentaires. L'objet de cette thèse est une méthode d'analyse et de synthèse des sons musicaux basée sur une réduction de données. Une telle méthode permet d'obtenir une représentation optimale - au sens de la variance - des sons musicaux. Cette représentation est, à la fois un puissant outil pour l'étude du timbre musical, mais aussi, la base d'une forme de synthèse efficace.}, } @BOOK{fukunaga90, author = {K. Fukunaga}, title = {{Introduction to Statistical Pattern Recognition}}, publisher = {Academic Press}, edition = {2}, year = {1990}, note = {}, remarks = {cited in \cite{cslu:icslp98cronk} for CART tree evaluation criterion. \tbf}, abstract = {}, } @INPROCEEDINGS{nock97, author = {H. J. Nock and M. J. F. Gales and Steve Young}, title = {A Comparative Study of Methods for Phonetic Decision-Tree State Clustering}, booktitle = {Proc. Eurospeech '97}, volume = {1}, address = {Rhodes, Greece}, month = {September}, year = {1997}, pages = {111--114}, remarks = {cited in \cite{cslu:esca98mm} for decision trees for speech recognition, \cite{cslu:icslp98cronk} for CART tree evaluation criterion. \tbf}, abstract = {}, }
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