15,490 research outputs found
Default ARTMAP
The default ARTMAP algorithm and its parameter values specified here define a ready-to-use general-purpose neural network system for supervised learning and recognition.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423) Office of Naval Research (N00014-01-1-0624
Point-set algorithms for pattern discovery and pattern matching in music
An algorithm that discovers the themes, motives and other perceptually significant repeated patterns in a musical work can be used, for example, in a music information retrieval system for indexing a collection of music documents so that it can be searched more rapidly. It can also be used in software tools for music analysis and composition and in a music transcription system or model of music cognition for discovering grouping structure, metrical structure and voice-leading structure. In most approaches to pattern discovery in music, the data is assumed to be in the form of strings. However, string-based methods become inefficient when one is interested in finding highly embellished occurrences of a query pattern or searching for polyphonic patterns in polyphonic music. These limitations can be avoided by representing the music as a set of points in a multidimensional Euclidean space. This point-set pattern matching approach allows the maximal repeated patterns in a passage of polyphonic music to be discovered in quadratic time and all occurrences of these patterns to be found in cubic time. More recently, Clifford et al. (2006) have shown that the best match for a query point set within a text point set of size n can be found in O(n log n) time by incorporating randomised projection, uniform hashing and FFT into the point-set pattern matching approach. Also, by using appropriate heuristics for selecting compact maximal repeated patterns with many non-overlapping occurrences, the point-set pattern discovery algorithms described here can be adapted for data compression. Moreover, the efficient encodings generated when this compression algorithm is run on music data seem to resemble the motivic-thematic analyses produced by human experts
Scanning and Sequential Decision Making for Multi-Dimensional Data - Part I: the Noiseless Case
We investigate the problem of scanning and prediction ("scandiction", for
short) of multidimensional data arrays. This problem arises in several aspects
of image and video processing, such as predictive coding, for example, where an
image is compressed by coding the error sequence resulting from scandicting it.
Thus, it is natural to ask what is the optimal method to scan and predict a
given image, what is the resulting minimum prediction loss, and whether there
exist specific scandiction schemes which are universal in some sense.
Specifically, we investigate the following problems: First, modeling the data
array as a random field, we wish to examine whether there exists a scandiction
scheme which is independent of the field's distribution, yet asymptotically
achieves the same performance as if this distribution was known. This question
is answered in the affirmative for the set of all spatially stationary random
fields and under mild conditions on the loss function. We then discuss the
scenario where a non-optimal scanning order is used, yet accompanied by an
optimal predictor, and derive bounds on the excess loss compared to optimal
scanning and prediction.
This paper is the first part of a two-part paper on sequential decision
making for multi-dimensional data. It deals with clean, noiseless data arrays.
The second part deals with noisy data arrays, namely, with the case where the
decision maker observes only a noisy version of the data, yet it is judged with
respect to the original, clean data.Comment: 46 pages, 2 figures. Revised version: title changed, section 1
revised, section 3.1 added, a few minor/technical corrections mad
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