Automating Operational Business Decisions Using Artificial
... The process of making business decisions is increasingly reliant upon analyzing very large data-sets. Due to the amount of decisions having to be made on a daily basis, this becomes time-consuming and expensive to carry out manually. The purpose of this thesis was to determine whether using Artifici ...
... The process of making business decisions is increasingly reliant upon analyzing very large data-sets. Due to the amount of decisions having to be made on a daily basis, this becomes time-consuming and expensive to carry out manually. The purpose of this thesis was to determine whether using Artifici ...
Finding the Frequent Items in Streams of Data
... Many data generation processes produce huge numbers of pieces of data, each of which is simple in isolation, but which taken together lead to a complex whole. Examples ...
... Many data generation processes produce huge numbers of pieces of data, each of which is simple in isolation, but which taken together lead to a complex whole. Examples ...
Literature Review on Feature Selection Methods for High
... In the feature subset-based method, the features are combined as possible combinations of feature subsets using any one of the searching strategies. Then, the feature subsets are evaluated using any one of the statistical measures or the supervised learning algorithms to observe the significance of ...
... In the feature subset-based method, the features are combined as possible combinations of feature subsets using any one of the searching strategies. Then, the feature subsets are evaluated using any one of the statistical measures or the supervised learning algorithms to observe the significance of ...
Learning Visual Representations for Perception
... Of course, learning cannot start from a blank slate; prior structure and learning biases are required. Geman et al. (1992) compellingly demonstrated the inescapable trade-off between flexibility of the learning methods and quantities of training data required for stable generalization, the bias/vari ...
... Of course, learning cannot start from a blank slate; prior structure and learning biases are required. Geman et al. (1992) compellingly demonstrated the inescapable trade-off between flexibility of the learning methods and quantities of training data required for stable generalization, the bias/vari ...
Getting Unique Solution in Data Exchange
... KRDB Research Center, Free University of Bolzano Bolzano, Italy ...
... KRDB Research Center, Free University of Bolzano Bolzano, Italy ...
Rule Insertion and Rule Extraction from Evolving Fuzzy
... neurons is HIGH or MODERATE) and (the sum of the incoming or outgoing connection weights is LOW) and (the neuron is NOT associated with the corresponding “yes” class output nodes (for classification tasks only) THEN the probability of pruning node (j) is HIGH. The above pruning rule is fuzzy and it ...
... neurons is HIGH or MODERATE) and (the sum of the incoming or outgoing connection weights is LOW) and (the neuron is NOT associated with the corresponding “yes” class output nodes (for classification tasks only) THEN the probability of pruning node (j) is HIGH. The above pruning rule is fuzzy and it ...
PCS: An Efficient Clustering Method for High
... For k partitions with exactly q subsets in each partition, there are totally kq number of sets and we can arrange them as a k by q matrix. The PCk optimization problem attempts to minimize the function of σ (Equation 2), which is the summation of symmetric differences of each pair of subsets within ...
... For k partitions with exactly q subsets in each partition, there are totally kq number of sets and we can arrange them as a k by q matrix. The PCk optimization problem attempts to minimize the function of σ (Equation 2), which is the summation of symmetric differences of each pair of subsets within ...
Data Splitting
... prediction and generalization capabilities [Mitchell, 1997]. In the case of supervised learning, a computational model is trained to predict outputs of an unknown target function. The target function is represented by a finite training dataset T of examples of inputs and the corresponding desired ou ...
... prediction and generalization capabilities [Mitchell, 1997]. In the case of supervised learning, a computational model is trained to predict outputs of an unknown target function. The target function is represented by a finite training dataset T of examples of inputs and the corresponding desired ou ...
Editing Statistical Records by Neural Networks
... The common aim of both the above paradigms is to capture the knowledge of experts and represent it in computerized systems to solve non-trivial mental tasks requiring expert knowledge. This is exactly the problem in the control and imputation of individual survey records. If the knowledge of an edit ...
... The common aim of both the above paradigms is to capture the knowledge of experts and represent it in computerized systems to solve non-trivial mental tasks requiring expert knowledge. This is exactly the problem in the control and imputation of individual survey records. If the knowledge of an edit ...
MP220
... another pH buffer (choice of 3) press Cal again. Press Cal repeatedly to exit the calibration routine. The decimal point flashes during calibration measurement. When the electrode output has stabilized the stability indicator appears. Manual endpoint - press Read to endpoint. Auto endpoint - the met ...
... another pH buffer (choice of 3) press Cal again. Press Cal repeatedly to exit the calibration routine. The decimal point flashes during calibration measurement. When the electrode output has stabilized the stability indicator appears. Manual endpoint - press Read to endpoint. Auto endpoint - the met ...