26197775
Using Neural Networks to Determine Sugeno Measures by Statistics
Abstract To replace the traditional weighted average method, Choquet integrals or Sugeno integrals with respect to fuzzy measures are used to obtain a synthetic evaluation of a given object (or its quality, function, etc. respectively) with multi-attribute. Generally, it is not easy to determine fuzzy measures in real problems due to the subjectivity of human thinking. It is even much more difficult than determining weights in the weighted average method, because of the nonadditivity of fuzzy measures. This paper uses a neural network algorithm to optimize the inverse problem of synthetic evaluation, and thus to determine Sugeno measures by the Choquet integral and statistics of given data. Since the Choquet integral is a generalization of the weighted average method, this technology has a broad applicability in areas of multivariate analysis, decision making, pattern recognition, image and speech processing and expert systems. Copyright © 1996 Elsevier Science Ltd.
2059324
Choquet integrals and natural extensions of lower probabilities
Abstract Both the Choquet integral with respect to monotone set functions and the natural extensions of lower probabilities are generalizations of the Lebesgue integral with respect to σ-additive measures. The relation between these generalizations is investigated. We show that the Choquet integral with respect to a belief measure is always greater than or equal to the corresponding natural extension. Also, we compare some concepts introduced in the theory of imprecise probabilities with concepts established in fuzzy measure theory, capacity theory, and Dempster-Shafer theory.
27330707
Some recent advances on the possibility measure theory
8746593
Modal logic interpretation of Dempster-Shafer theory: An infinite case
Abstract A modal logic interpretation of belief and plausibility measures defined on infinite sets is established. As a special case, a modal logic interpretation of necessity and possibility measures defined on infinite sets is also established. It is proven in both cases that the interpretation is complete. These results establish, in effect, modal logic interpretations of the Dempster-Shafer theory and possibility theory.
236203
New Techniques for DNA Sequence Classification
DNA sequence classification is the activity of determining whether or not an unlabeled sequence S belongs to an existing class C. This paper proposes two new techniques for DNA sequence classification. The first technique works by comparing the unlabeled sequence S with a group of active motifs discovered from the elements of C and by distinction with elements outside of C. The second technique generates and matches gapped fingerprints of S with elements of C. Experimental results obtained by running these algorithms on long and well conserved Alu sequences demonstrate the good performance of the presented methods compared with FASTA. When applied to less conserved and relatively short functional sites such as splice-junctions, a variation of the second technique combining fingerprinting with consensus sequence analysis gives better results than the current classifiers employing text compression and machine learning algorithms.
204983610
Pyramidic Clustering of Large-Scale Microarray Images
With ongoing research and development of imaging techniques such as those involved in brain MRIs, cDNA microarrays and satellite reconnaissance, the need for tools that can intelligently parse larger images is ever increasing. One group of such techniques often used is that of segmentation, an example of which is that of clustering algorithms. In order to deal with large data sets, current approaches require the data to be sampled or summarized before true analysis can take place. In this paper we propose a novel image analysis technique using pyramidic type grouping, namely copasetic clustering, which focuses on the problem of applying traditional clustering techniques to these large-scale image data sets with limited resources. A further benefit of the technique is the transparency of its intermediate clustering steps; when applied to spatial data sets this allows the capture and incorporation of contextual information to improve result accuracy. The algorithm achieves an ∼1--3 dB w-to-noise ratio when compared with the conventional techniques described.
32491059
Speech Driven MPEG-4 Based Face Animation via Neural Network
In this paper, some clustering and machine learning methods are combined together to learn the correspondence between speech acoustic and MPEG-4 based face animation parameters. The features of audio and image sequences can be extracted from the large recorded audio-visual database. The face animation parameter (FAP) sequences can be computed and then clustered to FAP patterns. An artificial neural network (ANN) was trained to map the linear predictive coefficients (LPC) and some prosodic features of an individual's natural speech to FAP patterns. The performance of our system shows that the proposed learning algorithm is suitable, which can greatly improve the realism of real time face animation during speech.
153966
Compiling for the Impulse memory controller
The Impulse memory controller provides an interface for remapping irregular or sparse memory accesses into dense accesses in the cache memory. This capability significantly increases processor cache and system bus utilization, and previous work shows performance improvements from a factor of 1.2 to 5 with current technology models for hand-coded kernels in a cycle-level simulator. To attain widespread use of any specialized hardware feature requires automating its use in a compiler. We present compiler cost models using dependence and locality analysis that determine when to use Impulse to improve performance based on the reduction in misses, the additional cost for misses in Impulse, and the fixed cost for setting up a remapping. We implement the cost models and generate the appropriate Impulse system calls in the Scale compiler framework. Our results demonstrate that our cost models correctly choose when and when not to use Impulse. We also combine and compare Impulse with our implementation of loop permutation for improving locality. If loop permutation can achieve the same dense access pattern as Impulse, we prefer it, since it has no overheads, but we show that the combination can yield better performance.
17592799
Blind separation of slow waves and spikes from gastrointestinal myoelectrical recordings
Myoelectrical recordings of the gut contain slow waves (slow rhythmicity) and spikes (fast rhythmicity). While the slow wave determines the frequency and propagation of gastrointestinal contractions, spike activities are directly associated with the contractions. Traditionally, spikes are extracted from the myoelectrical recording using high-pass/bandpass filters. Due to the sharp rising edge (high-frequency component) of the slow wave, the conventional method is not accurate in the separation of the slow wave and spikes, although it has been used for years. In this paper, a novel and fast blind source separation algorithm is developed. Experimental results show that the proposed method was able to accurately extract spike activities from the myoelectrical recordings obtained in dogs and that its performance was not affected by the high-frequency components of the slow wave.