building shape and texture models of diatoms for analysis
... The first experiment consisted of identifying diatoms whose images were not used for constructing the model. For this experiment we used the standard “leave one out” approach, where the model was trained on all the specimens apart from one and the remaining specimen was identified using the trained ...
... The first experiment consisted of identifying diatoms whose images were not used for constructing the model. For this experiment we used the standard “leave one out” approach, where the model was trained on all the specimens apart from one and the remaining specimen was identified using the trained ...
CS171 - Intro to AI - Discussion Section 4
... What action should the agent take? o Rational agent should maximize expected utility Expected cost of actions: E[ utility(a) ] = 30 P(c) – 50 [1 - P(c) ] E[ utility(b) ] = -100 P(c) Break even point? 30 P(c) – 50 + 50 P(c) = -100 P(c) 100 P(c) + 30 P(c) + 50 P(c) = 50 => P(c) = 50/180 ~ 0.28 If ...
... What action should the agent take? o Rational agent should maximize expected utility Expected cost of actions: E[ utility(a) ] = 30 P(c) – 50 [1 - P(c) ] E[ utility(b) ] = -100 P(c) Break even point? 30 P(c) – 50 + 50 P(c) = -100 P(c) 100 P(c) + 30 P(c) + 50 P(c) = 50 => P(c) = 50/180 ~ 0.28 If ...
DATA MINING AND DATA-DRIVEN MODELING APPROACHES TO
... accuracy of the function depends on the hydraulic residence time, the dispersion and the reactions in the reactors downstream of the divider, in addition to the variability of the signal. A systematic application on a wide range of synthetic systems that may be found on WWTPs shows that the error is ...
... accuracy of the function depends on the hydraulic residence time, the dispersion and the reactions in the reactors downstream of the divider, in addition to the variability of the signal. A systematic application on a wide range of synthetic systems that may be found on WWTPs shows that the error is ...
Representing and Querying Correlated Tuples in Probabilistic
... our query q does not contain two copies of the same relation and consists of only the above three operators. In the longer version of the paper [34] we describe redefinitions for all relational algebra operators that do not make these assumptions and can handle all relational algebra queries. • sele ...
... our query q does not contain two copies of the same relation and consists of only the above three operators. In the longer version of the paper [34] we describe redefinitions for all relational algebra operators that do not make these assumptions and can handle all relational algebra queries. • sele ...
ReverseTesting: An Efficient Framework to Select Wei Fan Ian Davidson
... the training set, and then to apply the model to the entire customer database. Because these offers are usually not given at random, the training set is not drawn from the same population as the test set. Therefore, a model constructed using this training set may not perform well for the entire popu ...
... the training set, and then to apply the model to the entire customer database. Because these offers are usually not given at random, the training set is not drawn from the same population as the test set. Therefore, a model constructed using this training set may not perform well for the entire popu ...
Identifying and Accounting for Task-Dependent Bias in Crowdsourcing
... certainty over all variables of the model. We show how the approach can detect and correct task-dependent worker bias so that accurate task labels can be accurately inferred from worker annotations. The methods can be used to infer the relationships among task features, workers’ biases, annotations ...
... certainty over all variables of the model. We show how the approach can detect and correct task-dependent worker bias so that accurate task labels can be accurately inferred from worker annotations. The methods can be used to infer the relationships among task features, workers’ biases, annotations ...
Graph-based Diagnostic Medical Decision Support System
... While only a subset of functions were modeled in this step of model generation, a more robust system of distribution fitting is envisioned. Such a system would attempt to fit multiple distributions to the value set, picking the distribution with the minimum error. This improvement can be accomplishe ...
... While only a subset of functions were modeled in this step of model generation, a more robust system of distribution fitting is envisioned. Such a system would attempt to fit multiple distributions to the value set, picking the distribution with the minimum error. This improvement can be accomplishe ...
What makes a good model of natural images?
... Many low-level vision algorithms assume a prior probability over images, and there has been great interest in trying to learn this prior from examples. Since images are very non Gaussian, high dimensional, continuous signals, learning their distribution presents a tremendous computational challenge. ...
... Many low-level vision algorithms assume a prior probability over images, and there has been great interest in trying to learn this prior from examples. Since images are very non Gaussian, high dimensional, continuous signals, learning their distribution presents a tremendous computational challenge. ...
The Principles of Probability and Statistics
... samostatných vědních disciplín. The applications of descriptive and mathematical statistics and probability theory in an investigation of the collective random phenomena are the subject of probability and statistics. To describe these applications it is necessary to first be concerned with descripti ...
... samostatných vědních disciplín. The applications of descriptive and mathematical statistics and probability theory in an investigation of the collective random phenomena are the subject of probability and statistics. To describe these applications it is necessary to first be concerned with descripti ...
Dissimilarity-based Sparse Subset Selection
... In this paper, we consider the problem of finding representatives, given pairwise dissimilarities between the elements of a source set, X, and a target set, Y, in an unsupervised framework. In order to find a few representatives of X that well encode the collection of elements of Y, we propose an op ...
... In this paper, we consider the problem of finding representatives, given pairwise dissimilarities between the elements of a source set, X, and a target set, Y, in an unsupervised framework. In order to find a few representatives of X that well encode the collection of elements of Y, we propose an op ...
TM-LDA: Efficient Online Modeling of the Latent Topic Transitions in
... causal relationships among social phenomena. For instance, the factors or topics leading to “Traffic” will be interesting to the traffic department. • Providing a signal of unusual events when topics fail to follow common transition rules. TM-LDA is designed to learn the topic transition parameters from ...
... causal relationships among social phenomena. For instance, the factors or topics leading to “Traffic” will be interesting to the traffic department. • Providing a signal of unusual events when topics fail to follow common transition rules. TM-LDA is designed to learn the topic transition parameters from ...
Probabilistic Label Trees for Efficient Large Scale Image
... multi-class tasks [1]. In this method, the tree structure is determined by the spectral clustering of the confusion matrix, which is generated by a one-against-all classifier. Then they learn the tree classifier based on a tree based loss function that outperforms independent nodes optimization. In ...
... multi-class tasks [1]. In this method, the tree structure is determined by the spectral clustering of the confusion matrix, which is generated by a one-against-all classifier. Then they learn the tree classifier based on a tree based loss function that outperforms independent nodes optimization. In ...
R u t c o r Research Logical Analysis of Multi-Class
... LAD is a pattern-based two-class learning algorithm which integrates principles of combinatorics, optimization and the theory of Boolean functions. The research area of LAD was introduced and developed by Peter L. Hammer [28] whose vision expanded the LAD methodology from theory to successful data a ...
... LAD is a pattern-based two-class learning algorithm which integrates principles of combinatorics, optimization and the theory of Boolean functions. The research area of LAD was introduced and developed by Peter L. Hammer [28] whose vision expanded the LAD methodology from theory to successful data a ...
