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48285 results in Computer Science

Page 1278 of 2415

  • 17 - Conclusions and Future Directions

  • from Part V - Conclusions
  • Masashi Sugiyama, Tokyo Institute of Technology, Taiji Suzuki, University of Tokyo, Takafumi Kanamori, Nagoya University, Japan
  • Book:
    Density Ratio Estimation in Machine Learning
    Published online:
    05 March 2012
    Print publication:
    20 February 2012, pp 303-306

  • Summary

    In this book we described a new approach to machine learning based on densityratio estimation. This density-ratio approach offers a novel research paradigm in the field of machine learning and data mining from theory and algorithms to application.

    In Part II, various methods for density-ratio estimation were described, including methods based on separate estimations of numerator and denominator densities (Chapter 2), moment matching between numerator and denominator samples (Chapter 3), probabilistic classifications of numerator and denominator samples (Chapter 4), density fitting between numerator and denominator densities (Chapter 5), and direct fitting of a density-ratio model to the true densityratio (Chapter 6). We also gave a unified framework of density-ratio estimation in Chapter 7, which accommodates the various methods described above and is substantially more general – as an example, a robust density-ratio estimator was derived. Finally, in Chapter 8, we described methods that combine density-ratio estimation with dimensionality reduction. Among various density-ratio estimators, the unconstrained least-squares importance fitting (uLSIF) method described in Chapter 6 would be most useful practically because of its high computational efficiency by an analytic-form solution, the availability of cross-validation for model selection, its wide applicability to various machine learning tasks (Part III), and its superior convergence and numerical properties (Part IV).

    In Part III we covered the usage of density-ratio estimators in various machine learning tasks that were categorized into four groups. In Chapter 9 we described applications of density ratios to importance sampling tasks such as non-stationarity/domain adaptation and multi-task learning.

  • OSO volume 17 issue 1 Cover and Front matter

  • Journal:
    Organised Sound / Volume 17 / Issue 1 / 14 February 2012
    Published online by Cambridge University Press:
    14 February 2012, pp. f1-f2
    Print publication:
    14 February 2012
    • Article
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  • Rethinking Collaboration in Networked Music*

  • Nicolas Makelberge
  • Journal:
    Organised Sound / Volume 17 / Issue 1 / 14 February 2012
    Published online by Cambridge University Press:
    14 February 2012, pp. 28-35
    Print publication:
    14 February 2012

  • This paper argues that today's sampling culture, emerging out of pioneering efforts in electroacoustic music in the 1950s carries a similar ethos of autonomy found in many significant advances in music instrumentation throughout history. By looking at the evolution of musical instruments, the author hopes to address these continuous effort towards autonomy, which, if proves legitimate should be of great concern for networked music research that deals with all forms of music praxis of varying reciprocity and group dynamics. By further looking into what sets collaboration apart from cooperation and collective creation, and elaborating on the ‘social’ of music, this paper hopes to extend the discourse on current trends of accessing, shaping and sharing music in solitude, from something often seen as unfortunate and anti-social, to something less so.

  • Composing for a Networked, Pulse-Based, Laptop Orchestra*

  • David Ogborn
  • Journal:
    Organised Sound / Volume 17 / Issue 1 / 14 February 2012
    Published online by Cambridge University Press:
    14 February 2012, pp. 56-61
    Print publication:
    14 February 2012

  • Guided by the idea of participatory culture, networked pulse synchronisation and live coding have been core approaches in the activity of the Cybernetic Orchestra, an electronic performance ensemble at McMaster University in Hamilton, Canada. Following general discussion of the way in which networked pulse-based music and live coding work within this orchestra, there is specific discussion of a number of compositional models and practices that have been found effective, including code-sharing, instruction-scores, code as material, and physical performance.

Page 1278 of 2415