Conferences related to Decision trees

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

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 papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)

IEEE CCNC 2020 will present the latest developments and technical solutions in the areas of home networking, consumer networking, enabling technologies (such as middleware) and novel applications and services. The conference will include a peer-reviewed program of technical sessions, special sessions, business application sessions, tutorials, and demonstration sessions.


2020 IEEE International Conference on Image Processing (ICIP)

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.


2020 IEEE International Conference on Industrial Technology (ICIT)

ICIT focuses on industrial and manufacturing applications of electronics, controls, communications, instrumentation, and computational intelligence.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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.


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Periodicals related to Decision trees

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Broadcasting, IEEE Transactions on

Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.


Circuits and Systems for Video Technology, IEEE Transactions on

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-- ...


Communications, IEEE Transactions on

Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...


Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.


Computers, IEEE Transactions on

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 ...


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Most published Xplore authors for Decision trees

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Xplore Articles related to Decision trees

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A novel approach to construct the binary decision tree based on consistent set

2006 International Conference on Machine Learning and Cybernetics, 2006

The document that should appear here is not currently available.


Improve Decision Support Using Adaptive Data Mining

2009 International Conference on Electrical, Communications, and Computers, 2009

Nowadays is necessary to take decisions based in the knowledge obtained through advanced techniques of date analysis, decision tree is an interesting option. In this work a Rich Internet application to visualize a decision tree in a mobile device is presented. This application lets deploy the complete tree decision and the categorization of new registers, with this tool is possible ...


HRRP Classification by Using Improved SVM Decision Tree

2006 6th World Congress on Intelligent Control and Automation, 2006

Support vector machine (SVM) has been used in high resolution range profile (HRRP) classification for its good generalization ability for the pattern classification problem with high feature dimension and small training set. In order to perform multi-class classification, decision-tree-based SVM was studied, the structure and the classification performance of the SVM decision tree was analyzed. A separability measure which based ...


An algorithm for designing a pattern classifier by using MDL criterion

Proceedings of 1995 IEEE International Symposium on Information Theory, 1995

The algorithm for designing a pattern classifier, which uses MDL criterion and a binary data structure, is proposed. The algorithm gives a partitioning of the space of the K-dimensional attribute and gives an estimated probability model for this partitioning. The volume of bins in this partitioning is asymptotically upper bounded by /spl Oscr/((log N/N)/sup K/(K+2/)/sup )/ for large N in ...


Fuzzy Decision Tree Algorithm for Customer Knowledge Management of PLM

2011 International Conference of Information Technology, Computer Engineering and Management Sciences, 2011

Compared with the information acquisition model based on product-centered in Customer Knowledge Management (CKM) of PLM (Product Life-Cycle Management), this paper proposes a new client-centered model which achieves the customer's ambiguous and uncertainty knowledge. In order to present the structure for the dataset which is from several the customer's tables, this paper proposes Fuzzy Decision Tree (FDT) algorithm for CKM ...


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Educational Resources on Decision trees

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IEEE.tv Videos

"What is Big Data Analytics and Why Should I Care?" - Big Data Analytics Tutorial Part 1
2015 IEEE Honors: IEEE Jack S. Kilby Signal Processing Medal - Harry L. Van Trees
An Introduction to Computational Intelligence in Multi-Criteria Decision-Making: The Intersection of Search, Preference Tradeoff
Bayesian Perception & Decision from Theory to Real World Applications
Fuzzy and Soft Methods for Multi-Criteria Decision Making - Ronald R Yager - WCCI 2016
Algorithmic Decision Making: Impacts and Implications - IEEE Internet Initiative Webinar
Fusing Simultaneously Acquired EEG and fMRI to Infer Spatiotemporal Dynamics of Cognition in the Human Brain - IEEE Brain Workshop
WIE: Our Own Voices - Noel Schulz, Kansas State University
Social Implications: Perils & Promises of AI - IEEE AI & Ethics Summit 2016
IEEE Authoring Parts 1 and 2: Publishing Choices
Robotics History: Narratives and Networks Oral Histories: Max Mintz
PGX Clinical Decision Support Implementation - Peter Hulick - IEEE EMBS at NIH, 2019
Crisis or Opportunity?: The Economic Impact on Underrepresented Communities - IEEE WIE ILC 2020 Virtual Series
A Conversation with…Francesca Rossi: IEEE TechEthics
Recurrent Neural Networks for System Identification, Forecasting and Control
Landing in a Self-Flying Airplane. Ready for it? - Antonio Crespo
Some Thoughts on a Gap Between Theory and Practice of Evolutionary Algorithms - WCCI 2012
Sensing and Decision Making in Social Networks
Photo Verification Technology for Radiology Images - Srini Tridandapani - IEEE EMBS at NIH, 2019
Diab and Frazier: Ethernet in the First Mile

IEEE-USA E-Books

  • A novel approach to construct the binary decision tree based on consistent set

    The document that should appear here is not currently available.

  • Improve Decision Support Using Adaptive Data Mining

    Nowadays is necessary to take decisions based in the knowledge obtained through advanced techniques of date analysis, decision tree is an interesting option. In this work a Rich Internet application to visualize a decision tree in a mobile device is presented. This application lets deploy the complete tree decision and the categorization of new registers, with this tool is possible to take decisions based in the analysis of data in an extended data base. The application is developed with the framework ldquoZKrdquo and requires a mobile device with Internet connection capability and a Web browser that support this kind of applications like: ldquoOpera mobilerdquo or ldquoSafari mobilerdquo.

  • HRRP Classification by Using Improved SVM Decision Tree

    Support vector machine (SVM) has been used in high resolution range profile (HRRP) classification for its good generalization ability for the pattern classification problem with high feature dimension and small training set. In order to perform multi-class classification, decision-tree-based SVM was studied, the structure and the classification performance of the SVM decision tree was analyzed. A separability measure which based on the distribution of the training samples was defined, the defined separability measure was applied into the formation of the decision tree, and an improved algorithm for SVM decision tree was proposed. The scheme of using the improved algorithm for SVM decision tree to classify HRRP was given. Experiments using the range profile datasets prove the effectiveness of our scheme

  • An algorithm for designing a pattern classifier by using MDL criterion

    The algorithm for designing a pattern classifier, which uses MDL criterion and a binary data structure, is proposed. The algorithm gives a partitioning of the space of the K-dimensional attribute and gives an estimated probability model for this partitioning. The volume of bins in this partitioning is asymptotically upper bounded by /spl Oscr/((log N/N)/sup K/(K+2/)/sup )/ for large N in probability, where N is the length of training sequence. The redundancy of the code length and the divergence of the estimated model are asymptotically upper bounded by /spl Oscr/(K(log N/N)/sup 2/(K+2/)/sup )/. The classification error is asymptotically upper bounded by /spl Oscr/(K/sup 1/2/(log N/N)/sup 1/(K+2/)/sup )/.

  • Fuzzy Decision Tree Algorithm for Customer Knowledge Management of PLM

    Compared with the information acquisition model based on product-centered in Customer Knowledge Management (CKM) of PLM (Product Life-Cycle Management), this paper proposes a new client-centered model which achieves the customer's ambiguous and uncertainty knowledge. In order to present the structure for the dataset which is from several the customer's tables, this paper proposes Fuzzy Decision Tree (FDT) algorithm for CKM to predict who the important or potential consumers are. The experimental result shows that the proposed method can be stronger and robust for classification and prediction in CKM.

  • Hierarchical mixtures of experts methodology applied to continuous speech recognition

    In this paper, we incorporate the hierarchical mixtures of experts (HME) method of probability estimation, developed by Jordan (1994), into a hidden Markov model (HMM)-based continuous speech recognition system. The resulting system can be thought of as a continuous-density HMM system, but instead of using Gaussian mixtures, the HME system employs a large set of hierarchically organized but relatively small neural networks to perform the probability density estimation. The hierarchical structure is reminiscent of a decision tree except for two important differences: each "expert" or neural net performs a "soft" decision rather than a hard decision, and, unlike ordinary decision trees, the parameters of all the neural nets in the HME are automatically trainable using the expectation-maximisation algorithm. We report results on the ARPA 5,000-word and 40,000-word Wall Street Journal corpus using HME models.

  • Evolutionary design of neural network trees with nodes of limited number of inputs

    Neural network tree (NNTree) is a decision tree (DT) with each non-terminal node being an expert neural network (ENN). Compared with conventional DTs, NNTrees can achieve good performance with less nodes and the performance can be improved further by incremental learning with new data. Currently, we find that it is also possible to extract comprehensible rules more easily from NNTrees than from conventional neural networks if the number of inputs of each ENN are limited. Usually, the time complexity for interpreting a neural network increases exponentially with the number of inputs. If we adopt NNTrees with nodes of limited number of inputs, the time complexity for extracting rules can become polynomial. In this paper, we introduce three methods for feature selection when the number of inputs is limited. The effectiveness of these methods is verified through experiments with four databases taken from the machine learning repository of the University of California at Irvine.

  • A comparison between decision trees and extension matrixes

    Decision trees and extension matrixes are two methodologies for (fuzzy) rule generation. This paper gives an initial study on the comparison between the two methodologies. Their computational complexity and the quality of rule generation are analyzed. The experimental results have shown that the number of generated rules of the heuristic algorithm based on extension matrix is fewer than the decision tree algorithm. Moreover, regarding the testing accuracy (i.e., the generalization capability for unknown cases), experiments have also shown that the extension matrix method is better than the other method.

  • Using machine learning for outcome prediction of patients with severe head injury

    The paper presents an application of decision tree induction to the problem of the prediction of outcome after a severe head injury. The study shows that induced decision trees are useful for the analysis of the importance of clinical parameters and of their combinations for the evaluation of the severity of brain injury and for outcome prediction.

  • Learning acceleration by policy sharing

    Reinforcement learning is one of the more prominent machine learning technologies due to its unsupervised learning structure and ability to continually learn, even in a dynamic operating environment. Applying this learning to cooperative multi-agent systems not only allows each individual agent to learn from its own experience, but also offers the opportunity for the individual agents to learn from the other agents in the system to increase the speed of learning can be accelerated. In the proposed learning algorithm, an agent store its experience in terms of state aggregation implemented with a decision tree, such that policy sharing between multi-agent is eventually accomplished by merging different decision trees between peers. Unlike lookup tables which have homogeneous structure for state aggregations, decision trees carried in agents are with heterogeneous structure. This work executes policy sharing between cooperative agents by means of forming a hyper structure from their trees instead of merging whole trees violently. The proposed scheme initially translates the whole decision tree from an agent to others. Based on the evidence, only partial leaf nodes hold helpful experience for policy sharing. The proposed method inducts a hyper decision tree by a great mount of samples which are sampled from the shared nodes. Results from simulations in multi-agent cooperative domain illustrate that the proposed algorithms perform better than the one without sharing.



Standards related to Decision trees

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No standards are currently tagged "Decision trees"