Kate A. Smith focuses on Artificial neural network, Combinatorial optimization, Data mining, Hopfield network and Artificial intelligence. Her Artificial neural network research is multidisciplinary, incorporating perspectives in Simulated annealing, Optimization problem, Mathematical optimization and Equity. Her Mathematical optimization study integrates concerns from other disciplines, such as Network model and Applied mathematics.
Fuzzy logic is closely connected to Cluster analysis in her research, which is encompassed under the umbrella topic of Data mining. Her work deals with themes such as Stability and Travelling salesman problem, which intersect with Hopfield network. The concepts of her Artificial intelligence study are interwoven with issues in Machine learning and Population-based incremental learning.
Kate A. Smith mainly investigates Artificial neural network, Artificial intelligence, Data mining, Mathematical optimization and Machine learning. Her work on Hopfield network as part of general Artificial neural network study is frequently linked to Maxima and minima, bridging the gap between disciplines. Her Hopfield network study incorporates themes from Recurrent neural network and Optimization problem.
Her Artificial intelligence research includes themes of Natural language processing and Pattern recognition. Her studies in Data mining integrate themes in fields like Algorithm, Order and Cluster analysis. Her Combinatorial optimization study combines topics in areas such as Stability, Self-organization and Stochastic neural network.
Kate A. Smith spends much of her time researching Artificial intelligence, Data mining, Association rule learning, Artificial neural network and Machine learning. Her studies deal with areas such as Natural language processing, Spike and Pattern recognition as well as Artificial intelligence. Her Data mining research incorporates themes from Time series, Correlation clustering, Cluster analysis, Information retrieval and Algorithm.
The Combinatorial optimization research Kate A. Smith does as part of her general Algorithm study is frequently linked to other disciplines of science, such as Intermittency, therefore creating a link between diverse domains of science. Her work in the fields of Artificial neural network, such as Backpropagation, Time delay neural network and Physical neural network, overlaps with other areas such as Generalization and Epoch. Her Algorithm Selection, Population-based incremental learning and Selection study in the realm of Machine learning connects with subjects such as Weighted Majority Algorithm.
Her primary areas of investigation include Data mining, Artificial intelligence, Best value, Association rule learning and Reduction. Her work carried out in the field of Data mining brings together such families of science as Correlation clustering, Canopy clustering algorithm, Cluster analysis, Fuzzy clustering and Data stream clustering. She interconnects Missing data and Curse of dimensionality in the investigation of issues within Correlation clustering.
The Artificial intelligence study combines topics in areas such as Machine learning and Natural language processing. Kate A. Smith usually deals with Machine learning and limits it to topics linked to No free lunch theorem and Artificial neural network. When carried out as part of a general Artificial neural network research project, her work on Softmax function is frequently linked to work in Maxima and minima, therefore connecting diverse disciplines of study.
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Characteristic-Based Clustering for Time Series Data
Xiaozhe Wang;Kate Smith;Rob Hyndman.
Data Mining and Knowledge Discovery (2006)
Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research
Kate A. Smith.
Informs Journal on Computing (1999)
On learning algorithm selection for classification
Shawkat Ali;Kate A. Smith.
soft computing (2006)
Neural networks in business: techniques and applications for the operations researcher
Kate A. Smith;Jatinder N.D. Gupta.
On chaotic simulated annealing
L. Wang;K. Smith.
IEEE Transactions on Neural Networks (1998)
Static and dynamic channel assignment using neural networks
K. Smith;M. Palaniswami.
IEEE Journal on Selected Areas in Communications (1997)
Web page clustering using a self-organizing map of user navigation patterns
Kate A. Smith;Alan Ng.
decision support systems (2003)
An Analysis of Customer Retention and Insurance Claim Patterns using Data Mining: A Case Study
Kate A Smith;Robert J Willis;Malcolm Brooks.
Journal of the Operational Research Society (2000)
Neural techniques for combinatorial optimization with applications
K. Smith;M. Palaniswami;M. Krishnamoorthy.
IEEE Transactions on Neural Networks (1998)
Neural Networks in Business: Techniques and Applications
Kate A. Smith;Jatinder N. D. Gupta.
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