A CONSTRUCTIVE ALGORITHM THAT CONVERGES FOR REAL-VALUED INPUT PATTERNS

1994 ◽  
Vol 05 (01) ◽  
pp. 59-66 ◽  
Author(s):  
NEIL BURGESS
Keyword(s):  
Constructive Algorithms ◽  
Constructive Algorithm ◽  
Cascade Correlation ◽  
Specific Error

A constructive algorithm is presented which combines the architecture of Cascade Correlation and the training of perceptron-like hidden units with the specific error-correcting roles of Upstart. Convergence to zero errors is proved for any consistent classification of real-valued pattern vectors. Addition of one extra element to each pattern allows hyper-spherical decision regions and enables convergence on real-valued inputs for existing constructive algorithms. Simulations demonstrate robust convergence and economical construction of hidden units in the benchmark “N-bit parity” and “twin spirals” problems.

3-D OBJECT CLASSIFICATION: APPLICATION OF A CONSTRUCTIVE ALGORITHM

1991 ◽  
Vol 02 (04) ◽  
pp. 275-282 ◽  
Author(s):  
Neil Burgess ◽  
Mario Notturno Granieri ◽  
Stefano Patarnello
Keyword(s):  
Object Classification ◽  
Arbitrary Orientation ◽  
Generalization Capability ◽  
Constructive Algorithm ◽  
Algorithm Convergence ◽  
Edge Points ◽  
Training Examples

A system for the classification of real 3-D objects is presented. Ten objects are presented in arbitrary orientation (and position, within limits). The perception of an object is achieved by the use of multiple stereo pairs of images taken from different view positions. Classification of the spectrum of distances between edge-points perceived on an object is achieved using a constructive algorithm. Convergence to zero errors on the set of training examples is guaranteed. The generalization capability is tested on a set of 10–15 novel presentations of each object.

scholarly journals Novel Methods Generated by Genetic Programming for the Guillotine-Cutting Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Vittorio Bertolini ◽  
Carlos Rey ◽  
Mauricio Sepulveda ◽  
Victor Parada
Keyword(s):  
Genetic Programming ◽  
Evolutionary Computation ◽  
State Of The Art ◽  
Two Dimensional ◽  
Constructive Algorithms ◽  
Constructive Algorithm ◽  
Novel Methods ◽  
Cutting Problem ◽  
New Algorithms ◽  
Guillotine Cutting

New constructive algorithms for the two-dimensional guillotine-cutting problem are presented. The algorithms were produced from elemental algorithmic components using evolutionary computation. A subset of the components was selected from a previously existing constructive algorithm. The algorithms’ evolution and testing process used a set of 46 instances from the literature. The structure of three new algorithms is described, and the results are compared with those of an existing constructive algorithm for the problem. Several of the new algorithms are competitive with respect to a state-of-the-art constructive algorithm. A subset of novel instructions, which are responsible for the majority of the new algorithms’ good performances, has also been found.

Application of the Cascade-Correlation network to the classification of circularly scanned images

1996 ◽  
Vol 4 (3) ◽  
pp. 161-167 ◽  
Author(s):  
Nikola Masic ◽  
Strahil D. Ristov ◽  
Bozidar Vojnovic
Keyword(s):  
Correlation Network ◽  
Cascade Correlation ◽  
Scanned Images

THE GENERALIZATION OF A CONSTRUCTIVE ALGORITHM IN PATTERN CLASSIFICATION PROBLEMS

1992 ◽  
Vol 03 (supp01) ◽  
pp. 65-70 ◽  
Author(s):  
Neil Burgess ◽  
Silvano Di Zenzo ◽  
Paolo Ferragina ◽  
Mario Notturno Granieri
Keyword(s):  
Principal Component Analysis ◽  
Maximum Likelihood ◽  
Pattern Classification ◽  
Principal Component ◽  
Network Size ◽  
Classification Problems ◽  
Constructive Algorithm ◽  
Maximum Likelihood Procedure ◽  
Optimal Network

The use of a constructive algorithm for pattern classification is examined. The algorithm, a ‘Perceptron Cascade’, has been shown to converge to zero errors whilst learning any consistent classification of real-valued pattern vectors (Burgess, 1992). Limiting network size and producing bounded decision regions are noted to be important for the generalization ability of a network. A scheme is suggested by which a result on generalization (Vapnik, 1992) may enable calculation of the optimal network size. A fast algorithm for principal component analysis (Sirat, 1991) is used to construct ‘hyper-boxes’ around each class of patterns to ensure bounded decision regions. Performance is compared with the Gaussian Maximum Likelihood procedure in three artificial problems simulating real pattern classification applications.

Note on the spectral classification of carbon stars in the photographic infrared region

1966 ◽  
Vol 24 ◽  
pp. 21-23
Author(s):  
Y. Fujita
Keyword(s):  
Infrared Region ◽  
Spectral Classification ◽  
Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

Diagnostic Value of Electron Microscopy in Soft Tissue Tumors

1970 ◽  
Vol 28 ◽  
pp. 220-221
Author(s):  
Gerald Fine ◽  
Azorides R. Morales
Keyword(s):  
Cardiac Myxoma ◽  
Osteogenic Sarcoma ◽  
Diagnostic Value ◽  
Soft Tissue Tumors ◽  
Amelanotic Melanoma ◽  
Growth Patterns ◽  
Light Microscopic Level ◽  
Mesenchymal Tumors ◽  
Good Agreement

For years the separation of carcinoma and sarcoma and the subclassification of sarcomas has been based on the appearance of the tumor cells and their microscopic growth pattern and information derived from certain histochemical and special stains. Although this method of study has produced good agreement among pathologists in the separation of carcinoma from sarcoma, it has given less uniform results in the subclassification of sarcomas. There remain examples of neoplasms of different histogenesis, the classification of which is questionable because of similar cytologic and growth patterns at the light microscopic level; i.e. amelanotic melanoma versus carcinoma and occasionally sarcoma, sarcomas with an epithelial pattern of growth simulating carcinoma, histologically similar mesenchymal tumors of different histogenesis (histiocytoma versus rhabdomyosarcoma, lytic osteogenic sarcoma versus rhabdomyosarcoma), and myxomatous mesenchymal tumors of diverse histogenesis (myxoid rhabdo and liposarcomas, cardiac myxoma, myxoid neurofibroma, etc.)

Ultrastructure of mesotheliomas

1984 ◽  
Vol 42 ◽  
pp. 98-101
Author(s):  
Irving Dardick
Keyword(s):  
Electron Microscopy ◽  
Latent Period ◽  
Spindle Cell ◽  
Spindle Cell Sarcoma ◽  
Metastatic Adenocarcinoma ◽  
Cell Sarcoma ◽  
Cytological Features ◽  
Industrial Use ◽  
Degree Of Differentiation

With the extensive industrial use of asbestos in this century and the long latent period (20-50 years) between exposure and tumor presentation, the incidence of malignant mesothelioma is now increasing. Thus, surgical pathologists are more frequently faced with the dilemma of differentiating mesothelioma from metastatic adenocarcinoma and spindle-cell sarcoma involving serosal surfaces. Electron microscopy is amodality useful in clarifying this problem.In utilizing ultrastructural features in the diagnosis of mesothelioma, it is essential to appreciate that the classification of this tumor reflects a variety of morphologic forms of differing biologic behavior (Table 1). Furthermore, with the variable histology and degree of differentiation in mesotheliomas it might be expected that the ultrastructure of such tumors also reflects a range of cytological features. Such is the case.

Interpreting HVEM of muscle-cell impulse networks

1992 ◽  
Vol 50 (1) ◽  
pp. 126-127
Author(s):  
Paul DeCosta ◽  
Kyugon Cho ◽  
Stephen Shemlon ◽  
Heesung Jun ◽  
Stanley M. Dunn
Keyword(s):  
Structural Analysis ◽  
Muscle Cell ◽  
Electron Micrographs ◽  
Relative Size ◽  
Automatic Classification ◽  
Nerve Fibers ◽  
Analysis Algorithm ◽  
Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

Dual stain kit for the microscopic gram classification of ocular infection bacteria

1993 ◽  
Vol 51 ◽  
pp. 248-249
Author(s):  
Jacob S. Hanker ◽  
Dale N. Holdren ◽  
Kenneth L. Cohen ◽  
Beverly L. Giammara
Keyword(s):  
Contact Lenses ◽  
Ocular Infection ◽  
Gram Negative Bacteria ◽  
Gram Negative ◽  
Gram Staining ◽  
Ocular Infections ◽  
Different Types ◽  
Culture Positive ◽  
Increased Susceptibility

Keratitis and conjunctivitis (infections of the cornea or conjunctiva) are ocular infections caused by various bacteria, fungi, viruses or parasites; bacteria, however, are usually prominent. Systemic conditions such as alcoholism, diabetes, debilitating disease, AIDS and immunosuppressive therapy can lead to increased susceptibility but trauma and contact lens use are very important factors. Gram-negative bacteria are most frequently cultured in these situations and Pseudomonas aeruginosa is most usually isolated from culture-positive ulcers of patients using contact lenses. Smears for staining can be obtained with a special swab or spatula and Gram staining frequently guides choice of a therapeutic rinse prior to the report of the culture results upon which specific antibiotic therapy is based. In some cases staining of the direct smear may be diagnostic in situations where the culture will not grow. In these cases different types of stains occasionally assist in guiding therapy.

Ultrastructural Classification of Pituary Adenomas

1990 ◽  
Vol 48 (3) ◽  
pp. 258-258
Author(s):  
S. Arumugam ◽  
Sarasa Bharati Arumugam
Keyword(s):  
Cytoplasmic Organelle ◽  
Null Cell ◽  
Cell Adenoma ◽  
Mixed Cell ◽  
Light Microscope Level ◽  
Null Cell Adenoma ◽  
Growth Hormone Cell ◽  
Ultrastructural Characteristics ◽  
Organelle Morphology

Adenoaas of the pituitary are no longer classified based on their tinctorial affinity to dyes. With the advent of the newer methods of sophisticated technology, it is now possible to classify. These depending upon the type of hormone secreted based either on histochemical techniques or on ultrastructural characteristics. The latter provides an insight into the cytoplasmic organelle morphology which offers a delightful feast to the eye as well.This paper presents the ultrastructural characters of the pituitary adenoma as seen in Madras. 171 adenomas (124 males and 47 females) were seen during 1972-1989, classified at the light microscope level as 159 chromophobe, 2 basophilic, 4 eosinophilic and 6 mixed adenomas.Ultrastructural examination showed that the sparsely granular prolactin cell adenoma is the commonest adenoma to be encountered closely followed by the growth hormone cell adenoma, null cell adenoma, the mixed cell adenoma and others.

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