649,435 research outputs found
Pattern recognition characterizations of micromechanical and morphological materials states via analytical quantitative ultrasonics
One potential approach to the quantitative acquisition of discriminatory information that can isolate a single structural state is pattern recognition. The pattern recognition characterizations of micromechanical and morphological materials states via analytical quantiative ultrasonics are outlined. The concepts, terminology, and techniques of statistical pattern recognition are reviewed. Feature extraction and classification and states of the structure can be determined via a program of ultrasonic data generation
Sparse Linear Models applied to Power Quality Disturbance Classification
Power quality (PQ) analysis describes the non-pure electric signals that are
usually present in electric power systems. The automatic recognition of PQ
disturbances can be seen as a pattern recognition problem, in which different
types of waveform distortion are differentiated based on their features.
Similar to other quasi-stationary signals, PQ disturbances can be decomposed
into time-frequency dependent components by using time-frequency or time-scale
transforms, also known as dictionaries. These dictionaries are used in the
feature extraction step in pattern recognition systems. Short-time Fourier,
Wavelets and Stockwell transforms are some of the most common dictionaries used
in the PQ community, aiming to achieve a better signal representation. To the
best of our knowledge, previous works about PQ disturbance classification have
been restricted to the use of one among several available dictionaries. Taking
advantage of the theory behind sparse linear models (SLM), we introduce a
sparse method for PQ representation, starting from overcomplete dictionaries.
In particular, we apply Group Lasso. We employ different types of
time-frequency (or time-scale) dictionaries to characterize the PQ
disturbances, and evaluate their performance under different pattern
recognition algorithms. We show that the SLM reduce the PQ classification
complexity promoting sparse basis selection, and improving the classification
accuracy
Pattern Recognition for Conditionally Independent Data
In this work we consider the task of relaxing the i.i.d assumption in pattern
recognition (or classification), aiming to make existing learning algorithms
applicable to a wider range of tasks. Pattern recognition is guessing a
discrete label of some object based on a set of given examples (pairs of
objects and labels). We consider the case of deterministically defined labels.
Traditionally, this task is studied under the assumption that examples are
independent and identically distributed. However, it turns out that many
results of pattern recognition theory carry over a weaker assumption. Namely,
under the assumption of conditional independence and identical distribution of
objects, while the only assumption on the distribution of labels is that the
rate of occurrence of each label should be above some positive threshold.
We find a broad class of learning algorithms for which estimations of the
probability of a classification error achieved under the classical i.i.d.
assumption can be generalised to the similar estimates for the case of
conditionally i.i.d. examples.Comment: parts of results published at ALT'04 and ICML'0
An Overview of the Use of Neural Networks for Data Mining Tasks
In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks
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