Classification tree analysis
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)
The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.
2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...
Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)
Methods, algorithms, and human-machine interfaces for physical and logical design, including: planning, synthesis, partitioning, modeling, simulation, layout, verification, testing, and documentation of integrated-circuit and systems designs of all complexities. Practical applications of aids resulting in producible analog, digital, optical, or microwave integrated circuits are emphasized.
Design and analysis of algorithms, computer systems, and digital networks; methods for specifying, measuring, and modeling the performance of computers and computer systems; design of computer components, such as arithmetic units, data storage devices, and interface devices; design of reliable and testable digital devices and systems; computer networks and distributed computer systems; new computer organizations and architectures; applications of VLSI ...
Proceedings. IEEE International Symposium on Information Theory, 1993
This talk is a survey of binary tree-structured methods for classification, regression, survival analysis, and clustering. The discussion will include a survey of unifying themes, together with applications, and an introduction to mathematical issues that arise in studying their asymptotic properties. There will be special emphasis on the CAR/sup TM/ algorithms of Breiman et al., and on applications of the ...
Proceedings. 1991 IEEE International Symposium on Information Theory, 1991
Proceedings of the (19th) International Conference on Software Engineering, 1997
Proceedings. 1991 IEEE International Symposium on Information Theory, 1991
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1999
Neural networks and decision tree methods are two common approaches to pattern classification. While neural networks can achieve high predictive accuracy rates, the decision boundaries they form are highly nonlinear and generally difficult to comprehend. Decision trees, on the other hand, can be readily translated into a set of rules. In this paper, we present a novel algorithm for generating ...
Computing Based on Material Training: Application to Binary Classification Problems - IEEE Rebooting Computing 2017
ICASSP 2012 Plenary-Dr. Stephane Mallat
Solving Sparse Representation for Image Classification using Quantum D-Wave 2X Machine - IEEE Rebooting Computing 2017
Playing Games with Computational Intelligence
IMS 2012 Microapps - Improve Microwave Circuit Design Flow Through Passive Model Yield and Sensitivity Analysis
IMS 2011 Microapps - A Practical Approach to Verifying RFICs with Fast Mismatch Analysis
IMS MicroApps: Multi-Rate Harmonic Balance Analysis
IMS 2011 Microapps - Yield Analysis During EM Simulation
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware - Emre Neftci: 2016 International Conference on Rebooting Computing
Spectrum Analysis: RF Boot Camp
Surgical Robotics: Analysis and Control Architecture for Semiautonomous Robotic Surgery
IMS 2012 Microapps - Generation and Analysis Techniques for Cost-efficient SATCOM Measurements Richard Overdorf, Agilent
Similarity and Fuzzy Logic in Cluster Analysis
New Approach of Vehicle Electrification: Analysis of Performance and Implementation Issue
A Flexible Testbed for 5G Waveform Generation and Analysis: MicroApps 2015 - Keysight Technologies
Deeper Neural Networks - Kurt Keutzer - LPIRC 2019
Collaborative Filtering II
Learning with Kernels for Streams of Structured Data
IMS 2011 Microapps - STAN Tool: A New Method for Linear and Nonlinear Stability Analysis of Microwave Circuits
This talk is a survey of binary tree-structured methods for classification, regression, survival analysis, and clustering. The discussion will include a survey of unifying themes, together with applications, and an introduction to mathematical issues that arise in studying their asymptotic properties. There will be special emphasis on the CAR/sup TM/ algorithms of Breiman et al., and on applications of the clustering algorithms to predictive, pruned, tree- structured vector quantization (predictive PTSVQ). The talk is a summary of collaborations with many authors over an eighteen year period.
Neural networks and decision tree methods are two common approaches to pattern classification. While neural networks can achieve high predictive accuracy rates, the decision boundaries they form are highly nonlinear and generally difficult to comprehend. Decision trees, on the other hand, can be readily translated into a set of rules. In this paper, we present a novel algorithm for generating oblique decision trees that capitalizes on the strength of both approaches. Oblique decision trees classify the patterns by testing on linear combinations of the input attributes. As a result, an oblique decision tree is usually much smaller than the univariate tree generated for the same domain. Our algorithm consists of two components: connectionist and symbolic. A three- layer feedforward neural network is constructed and pruned, a decision tree is then built from the hidden unit activation values of the pruned network. An oblique decision tree is obtained by expressing the activation values using the original input attributes. We test our algorithm on a wide range of problems. The oblique decision trees generated by the algorithm preserve the high accuracy of the neural networks, while keeping the explicitness of decision trees. Moreover, they outperform univariate decision trees generated by the symbolic approach and oblique decision trees built by other approaches in accuracy and tree size.
Several applications of statistical tree-based modelling are described to problems in speech and language, including prediction of possible phonetic realizations, segment duration modelling in speech synthesis and end of sentence detection in text analysis.
High software reliability is an important attribute of high-assurance systems. Software quality models yield timely predictions of reliability indicators on a module-by-module basis, enabling one to focus on finding faults early in development. This paper introduces the CART (Classification And Regression Trees) algorithm to practitioners in high-assurance systems engineering. This paper presents practical lessons learned in building classification trees for software quality modeling, including an innovative way to control the balance between misclassification rates. A case study of a very large telecommunications system used CART to build software quality models. The models predicted whether or not modules would have faults discovered by customers, based on various sets of software product and process metrics as independent variables. We found that a model based on two software product metrics had an accuracy that was comparable to a model based on 40 product and process metrics.
A fuzzy decision tree is constructed by allowing the possibility of partial membership of a point in the nodes that make up the tree structure. This extension of its expressive capabilities transforms the decision tree into a powerful functional approximant that incorporates features of connectionist methods, while remaining easily interpretable. Fuzzification is achieved by superimposing a fuzzy structure over the skeleton of a CART decision tree. A training rule for fuzzy trees, similar to backpropagation in neural networks, is designed. This rule corresponds to a global optimization algorithm that fixes the parameters of the fuzzy splits. The method developed for the automatic generation of fuzzy decision trees is applied to both classification and regression problems. In regression problems, it is seen that the continuity constraint imposed by the function representation of the fuzzy tree leads to substantial improvements in the quality of the regression and limits the tendency to overfitting. In classification, fuzzification provides a means of uncovering the structure of the probability distribution for the classification errors in attribute space. This allows the identification of regions for which the error rate of the tree is significantly lower than the average error rate, sometimes even below the Bayes misclassification rate.
Strong analogies between relational structures involving some composition operators and a certain class of neural networks are described. The problem of learning the connections of the structure is addressed, and relevant learning procedures are proposed. An optimized performance index which has a strong logical flavor is proposed. Some significant implementation details are studied. Numerical examples illustrate various schemes of learning in relational structures of different levels of complexity.<<ETX>>
Software quality models are tools for focusing software enhancement efforts. Such efforts are essential for mission-critical embedded software, such as telecommunications systems, because customer-discovered faults have very serious consequences and are very expensive to repair. We present an empirical study that evaluated software quality models over several releases to address the question, "How long will a model yield useful predictions?" We also introduce the Classification And Regression Trees (CART) algorithm to software reliability engineering practitioners. We present our method for exploiting CART features to achieve a preferred balance between the two types of misclassification rates. This is desirable because misclassifications of fault-prone modules often have much more severe consequences than misclassifications of those that are not fault-prone. We developed two classification-tree models based on four consecutive releases of a very large legacy telecommunications system. Forty-two software product, process, and execution metrics were candidate predictors. The first software quality model used measurements of the first release as the training data set and measurements of the subsequent three releases as evaluation data sets. The second model used measurements of the second release as the training data set and measurements of the subsequent two releases as evaluation data sets. Both models had accuracy that would be useful to developers.
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