Bach Chorales Data Set
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Matthew Brand. Pattern discovery via entropy minimization. MERL -- A MITSUBISHI ELECTRIC RESEARCH LABORATORY. 1998.
occupant. An HMM conventionally estimated from the same initial conditions is fully connected and thus too bushy to profitably illustrate or interpret. Prediction in Bach chorales We obtained a dataset of melodic lines from 100 of J.S. Bach's 371 surviving chorales from the UCI repository [Merz and Murphy, 1998], and transposed all into the key of C. We compared entropically and conventionally
Matthew Brand. An Entropic Estimator for Structure Discovery. NIPS. 1998.
To explore the practical utility of this framework, we will use entropically estimated HMMs as a window into the hidden structure of some human-generated time-series. Bach Chorales We obtained a dataset of melodic lines from 100 of J.S. Bach's 371 surviving chorales from the UCI repository [Merz and Murphy, 1998], and transposed all into the key of C. We compared entropically and conventionally
Zoubin Ghahramani and Michael I. Jordan. Factorial Hidden Markov Models. Machine Learning, 29. 1997.
HMMs of varying sizes (K ranging from 2 to 6; M ranging from 2 to 9) were also trained on the same data. To approximate the FACTORIAL HIDDEN MARKOV MODELS 17 Table 2. Attributes in the Bach chorale data set. The key signature and time signature attributes were constant over the duration of the chorale. All attributes were treated as real numbers and modeled using the linear-Gaussian observation model
Mohammed Waleed Kadous. Expanding the Scope of Concept Learning Using Metafeatures. School of Computer Science and Engineering, University of New South Wales.
a custom learner works, but is labour-intensive. Relational learning techniques tend to be very sensitive to noise and to the particular clausal representation selected. They are typically 1 These datasets are: arrythmia, audiology, bach chorales echocardiogram, isolet, mobile robots, waveform. unable to process large data sets in a reasonable time frame, and/or require the user to set limits on the
Mohammed Waleed Kadous and Claude Sammut. The University of New South Wales School of Computer Science and Engineering Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series.
Saito [Sai94], and further worked on by Manganaris [Man97]. The task is to classify a stream as one of three classes, cylinder (c), bell (b) or funnel (f ). Samples are generated as follows: 2 These datasets are: arrythmia, audiology, bach chorales echocardiogram, isolet, mobile robots, waveform. 6. Experimental Evaluation 161 c(t) = (6 + #) · # [a,b] (t) + #(t) b(t) = (6 + #) · # [a,b] (t) · (t -