Search Results - "Polikar, R."

Incremental Learning of Concept Drift in Nonstationary Environments by Elwell, R., Polikar, R.

“…We introduce an ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the…”
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Learn ^ .NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes by Muhlbaier, M.D., Topalis, A., Polikar, R.

“…We have previously introduced an incremental learning algorithm Learn ++ , which learns novel information from consecutive data sets by generating an ensemble…”
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Twenty-Year Trends in the Incidence and Outcome of Cardiogenic Shock in AMIS Plus Registry by Hunziker, Lukas, Radovanovic, Dragana, Jeger, Raban, Pedrazzini, Giovanni, Cuculi, Florim, Urban, Philip, Erne, Paul, Rickli, Hans, Pilgrim, Thomas, Hess, F, Simon, R, Hangartner, P.J, Hufschmid, U, Hornig, B, Altwegg, L, Trummler, S, Windecker, S, Rueff, T, Loretan, P, Roethlisberger, C, Evéquoz, D, Mang, G, Ryser, D, Müller, P, Jecker, R, Kistler, W, Hongler, T, Stäuble, S, Freiwald, G, Schmid, H.P, Stauffer, J.C, Cook, S, Bietenhard, K, Roffi, M, Wojtyna, W, Schönenberger, R, Simonin, C, Waldburger, R, Schmidli, M, Federspiel, B, Weiss, E.M, Marty, H, Weber, K, Zender, H, Poepping, I, Hugi, A, Koltai, E, Iglesias, J.F, Erne, P, Heimes, T, Jordan, B, Pagnamenta, A, Feraud, P, Beretta, E, Stettler, C, Repond, F, Widmer, F, Heimgartner, C, Polikar, R, Bassetti, S, Iselin, H.U, Giger, M, Egger, P, Kaeslin, T, Fischer, A, Herren, T, Eichhorn, P, Neumeier, C, Flury, G, Girod, G, Vogel, R, Niggli, B, Yoon, S, Nossen, J, Stoller, U, Veragut, U.P, Bächli, E, Weber, A, Schmidt, D, Hellermann, J, Eriksson, U, Fischer, T, Peter, M, Gasser, S, Fatio, R, Vogt, M, Ramsay, D, Wyss, C, Bertel, O, Maggiorini, M, Eberli, F, Christen, S

“…BACKGROUND:Long-term trends of the incidence and outcome of cardiogenic shock (CS) patients are scarce. We analyze for the first time trends in the incidence…”
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Learn++: an incremental learning algorithm for supervised neural networks by Polikar, R., Upda, L., Upda, S.S., Honavar, V.

“…We introduce Learn++, an algorithm for incremental training of neural network (NN) pattern classifiers. The proposed algorithm enables supervised NN paradigms,…”
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An Ensemble-Based Incremental Learning Approach to Data Fusion by Parikh, D., Polikar, R.

“…This paper introduces Learn++, an ensemble of classifiers based algorithm originally developed for incremental learning, and now adapted for information/data…”
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Local Classifier Weighting by Quadratic Programming by Cevikalp, H., Polikar, R.

“…It has been widely accepted that the classification accuracy can be improved by combining outputs of multiple classifiers. However, how to combine multiple…”
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An architecture for intelligent systems based on smart sensors by Schmalzel, J., Figueroa, F., Morris, J., Mandayam, S., Polikar, R.

“…Based on requirements for a next-generation rocket test facility, elements of a prototype intelligent rocket test facility (IRTF) have been implemented. The…”
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The thyroid and the heart by POLIKAR, R, BURGER, A. G, SCHERRER, U, NICOD, P

“…Cardiovascular manifestations are a frequent finding in hyperthyroid and hypothyroid states. In this review, potential mechanisms by which thyroid hormones may…”
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Model comparison for automatic characterization and classification of average ERPs using visual oddball paradigm by Merzagora, A.C, Butti, M, Polikar, R, Izzetoglu, M, Bunce, S, Cerutti, S, Bianchi, A.M, Onaral, B

“…Abstract Objective To determine whether automated classifiers can be used for correctly identifying target categorization responses from averaged event-related…”
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An incremental learning algorithm with confidence estimation for automated identification of NDE signals by Polikar, R., Udpa, L., Udpa, S., Honavar, V.

“…An incremental learning algorithm is introduced for learning new information from additional data that may later become available, after a classifier has…”
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Frequency invariant classification of ultrasonic weld inspection signals by Polikar, R., Udpa, L., Udpa, S.S., Taylor, T.

“…Automated signal classification systems are finding increasing use in many applications for the analysis and interpretation of large volumes of signals. Such…”
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Learning concept drift in nonstationary environments using an ensemble of classifiers based approach by Karnick, M., Ahiskali, M., Muhlbaier, M.D., Polikar, R.

“…We describe an ensemble of classifiers based approach for incrementally learning from new data drawn from a distribution that changes in time, i.e., data…”
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Detection and identification of odorants using an electronic nose by Polikar, R., Shinar, R., Honavar, V., Udpa, L., Porter, M.D.

“…Gas sensing systems for detection and identification of odorant molecules are of crucial importance in an increasing number of applications. Such applications…”
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A generalized likelihood ratio technique for automated analysis of bobbin coil eddy current data by Das, M, Shekhar, H, Liu, X, Polikar, R, Ramuhalli, P, Udpa, L, Udpa, S

“…This paper presents a generalized likelihood ratio technique for detection of defect locations from bobbin coil eddy current data. First a Neyman–Pearson (NP)…”
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LEARN++: an incremental learning algorithm for multilayer perceptron networks by Polikar, R., Udpa, L., Udpa, S.S., Honavar, V.

“…We introduce a supervised learning algorithm that gives neural network classification algorithms the capability of learning incrementally from new data without…”
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Short- and long-term clinical outcome after Q wave and non-Q wave myocardial infarction in a large patient population by NICOD, P, GILPIN, E, DITTRICH, H, POLIKAR, R, HJALMARSON, A, BLACKY, A. R, HENNING, H, ROSS, J. JR

“…Prognosis for patients with non-Q wave myocardial infarction is controversial although a number of studies have shown a less favorable outlook after hospital…”
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Twenty-year trends in the prevalence of modifiable cardiovascular risk factors in young acute coronary syndrome patients hospitalized in Switzerland by Mahendiran, Thabo, Hoepli, André, Foster-Witassek, Fabienne, Rickli, Hans, Roffi, Marco, Eberli, Franz, Pedrazzini, Giovanni, Jeger, Raban, Radovanovic, Dragana, Fournier, Stephane

“…Abstract Aims Modifiable cardiovascular risk factors (RFs) play a key role in the development of coronary artery disease. We evaluated 20-year trends in RF…”
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Combining classifiers for multisensor data fusion by Parikh, D., Kim, M.T., Oagaro, J., Mandayam, S., Polikar, R.

“…Learn++ was recently introduced as an ensemble of classifiers based incremental learning algorithm, capable of retaining formerly acquired knowledge while…”
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Fuzzy ARTMAP network with evolutionary learning by Ramuhalli, P., Polikar, R., Udpa, L., Udpa, S.S.

“…Neural networks, particularly the multilayer perceptron, have been used extensively in automated signal classification systems with classification accuracy as…”
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An ensemble of classifiers approach for the missing feature problem by Krause, S., Polikar, R.

“…A new learning algorithm is introduced that can accommodate data with missing features. The algorithm uses an ensemble of classifiers approach. The classifiers…”
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