pattern classificationの例文

• SIT began as a quantitative model of visual pattern classification.
• Neural networks can be used to assist in pattern classification, forecasting and marketing analysis.
• It allows the user to select the features and pattern classification parameters for the automatic processing of these large image sets.
• Hart and Richard O . Duda are the authors of " Pattern Classification and Scene Analysis ", originally published in 1973.
• For example, spatial filtering algorithms will determine how to best utilize information from the different electrodes, and pattern classification may categorize the resulting data.
• The RSM using statistical pattern classification where examinees'observed response patterns are matched to pre-determined response patterns that each correspond to a particular cognitive or knowledge state.
• Dimension of signal space, n, is often too large to be useful for practical application such as pattern classification, we need to transform the signal space into a space with lower dimensionality.
• In general, the user will load the images, specify the classes, select the features, select the test set, choose the pattern classification parameters and then let the program process the entire image set.
• An optional ( and very useful ) feature of fuzzy ART is complement coding, a means of incorporating the absence of features into pattern classifications, which goes a long way towards preventing inefficient and unnecessary category proliferation.
• Proper scoring rules are used in meteorology, finance, and pattern classification where a forecaster or algorithm will attempt to minimize the average score to yield refined, calibrated probabilities ( i . e . accurate probabilities ).
• The primary function of this tool is to explore feature extraction and pattern classification and allow the user to perform batch processing with large image sets and is thus much more efficient than processing one image at a time with CVIPtools.
• ISODATA is defined in the abstract as :'a novel method of data analysis and pattern classification, is described in verbal and pictorial terms, in terms of a two-dimensional example, and by giving the mathematical calculations that the method uses.
• In the computational approach to quantum neural network research, scientists try to combine artificial neural network models ( which are widely used in machine learning for the important task of pattern classification ) with the advantages of quantum information in order to develop more efficient algorithms ( for a review, see ).
• Tas Venetsanopoulos'research interests included : Biometric Research, Multimedia ( image compression, image and video retrieval ); Digital Signal / Image Processing ( multichannel image processing, nonlinear, adaptive and M-D filtering, knowledge based image processing and recognition, 3-D imaging, biomedical applications ); Pattern Classification and Telecommunications.
• This is called "'bias-variance tradeoff "'or the "'bias-variance theorem "'and is covered in most introductory books on AI and pattern classification, though I can't guarantee you'll grasp it quickly or intuitively . ( I certainly didn't : -)
• The Iterative Self-Organizing Data Analysis Technique ( ISODATA ) algorithm used for Multispectral pattern recognition was developed by Geoffrey H . Ball and David J . Hall, working in the Stanford Research Institute in Menlo Park, CA . They published their findings in a technical report entitled : ISODATA, a novel method of data analysis and pattern classification ( Stanford Research Institute, 1965 ).
• Databases are currently being developed and maintained for research purposes by Jantz for students and faculty, which include Boas anthropometrics which include body measurements on 15, 000 BP Native Americans and 2, 000 BP Siberians, Forensic-osteometric and other forensic data from 1, 500 BP recent American skeletons, Heinz Brehme Dermatoglyphic Database which includes ridge-counts, pattern classifications on 50, 000 BP people from most parts of the world, Plains osteometric cranial and postcranial morphometric data on 2, 000 BP individuals from the Great Plains region, the Great Basin, the Southwest and Northwest.