VISUAL ANALYTICS OF MANUFACTURING SIMULATION DATA
... A prerequisite for visual analytics is the clustering of the datasets under investigation. In conjunction with simulation data analysis, we propose to group individual simulation runs into clusters of similar output performance values. The first question to answer is therefore which output parameter ...
... A prerequisite for visual analytics is the clustering of the datasets under investigation. In conjunction with simulation data analysis, we propose to group individual simulation runs into clusters of similar output performance values. The first question to answer is therefore which output parameter ...
Pareto Density Estimation: A Density Estimation for Knowledge
... The detection of the cluster structure in an input data set using distance structures is only possible, if most of the data distances within a cluster are smaller than the distances measured between data from different clusters. Let v denote the ratio of intra cluster distances to inter distances. I ...
... The detection of the cluster structure in an input data set using distance structures is only possible, if most of the data distances within a cluster are smaller than the distances measured between data from different clusters. Let v denote the ratio of intra cluster distances to inter distances. I ...
Applying Data Mining Methods for the Analysis of Stable Isotope
... Domain scientists have been discussing the following questions: What is the role of oxygen in the model of the sample distribution? Can we omit oxygen from the analysis and combine the datasets? Many more questions about the attributes: If we want to include spatial data (build a map), how is the ...
... Domain scientists have been discussing the following questions: What is the role of oxygen in the model of the sample distribution? Can we omit oxygen from the analysis and combine the datasets? Many more questions about the attributes: If we want to include spatial data (build a map), how is the ...
Recovery of non-linear cause-effect relationships
... where HSIC(A, B) denotes the empirical HSIC estimate8 and Rw,σ,θ corresponds to the residuals Yw − r(C1 ) using kernel ridge regression with Gaussian kernel of width |σ| and ridge regression parameter |θ|. To temper overfitting, the sample is split into three partitions; the residuals of the ith par ...
... where HSIC(A, B) denotes the empirical HSIC estimate8 and Rw,σ,θ corresponds to the residuals Yw − r(C1 ) using kernel ridge regression with Gaussian kernel of width |σ| and ridge regression parameter |θ|. To temper overfitting, the sample is split into three partitions; the residuals of the ith par ...
A review of artificial intelligence applications in
... contrast to most physically based models that use the first principles (e.g. physical laws) to derive the underlying relationships of the system, which usually justifiably simplified with many assumptions and require prior knowledge about the nature of the relationships among the data. This is one o ...
... contrast to most physically based models that use the first principles (e.g. physical laws) to derive the underlying relationships of the system, which usually justifiably simplified with many assumptions and require prior knowledge about the nature of the relationships among the data. This is one o ...
Selecting Representative Data Sets
... and measures for the model evaluation are described in section 2. For a reliable future error prediction we need to evaluate our model on a different, independent and identically distributed set that is different to the set that we have used for building the model. In absence of an independent ident ...
... and measures for the model evaluation are described in section 2. For a reliable future error prediction we need to evaluate our model on a different, independent and identically distributed set that is different to the set that we have used for building the model. In absence of an independent ident ...
BAYESIAN STATISTICS
... the conditions under which they are valid), or proving the logical inconsistency of others. The main consequence of these foundations is the mathematical need to describe by means of probability distributions all uncertainties present in the problem. In particular, unknown parameters in probability ...
... the conditions under which they are valid), or proving the logical inconsistency of others. The main consequence of these foundations is the mathematical need to describe by means of probability distributions all uncertainties present in the problem. In particular, unknown parameters in probability ...
Toward General Analysis of Recursive Probability Models
... language. For expressions that do not contain distributions, the semantics (like the syntax) of the language is the same as that of the normal A-calculus. We have extended this semantics to handle distributions. A distribution may be thought of as a variable whose value will be determined randomly. ...
... language. For expressions that do not contain distributions, the semantics (like the syntax) of the language is the same as that of the normal A-calculus. We have extended this semantics to handle distributions. A distribution may be thought of as a variable whose value will be determined randomly. ...
Visual Reconciliation of Alternative Similarity Spaces
... sources (e.g., fires, farmlands) and sinks (e.g., forests, oceans) to explicit ecosystem processes. Each TBM is defined by the different input parameters for characterizing these processes and outputs that quantify the dependency between the carbon cycle and the ecosystem processes. In the context o ...
... sources (e.g., fires, farmlands) and sinks (e.g., forests, oceans) to explicit ecosystem processes. Each TBM is defined by the different input parameters for characterizing these processes and outputs that quantify the dependency between the carbon cycle and the ecosystem processes. In the context o ...
A General Framework for Mining Concept-Drifting Data
... Building a descriptive model does not require prior knowledge about the form of data distribution. The data speaks for itself. Also, no matter how the underlying distribution changes, the descriptive model would be easily adapted to the new distribution. 2.3 Probability Estimation vs. Label Predicti ...
... Building a descriptive model does not require prior knowledge about the form of data distribution. The data speaks for itself. Also, no matter how the underlying distribution changes, the descriptive model would be easily adapted to the new distribution. 2.3 Probability Estimation vs. Label Predicti ...
Multi-objective optimization of support vector machines
... gradient-based approaches [14, 15, 16, 17, 18]. When applicable, these methods are highly efficient. However, they have some drawbacks and limitations. The most important one is that the score function for assessing the performance of the hyperparameters (or at least an accurate approximation of thi ...
... gradient-based approaches [14, 15, 16, 17, 18]. When applicable, these methods are highly efficient. However, they have some drawbacks and limitations. The most important one is that the score function for assessing the performance of the hyperparameters (or at least an accurate approximation of thi ...
FAKULTAS TEKNIK UNIVERSITAS NEGERI YOGYAKARTA LAB
... Nearest Neighbor (also known as Collaborative Filtering or Instance-based Learning) is a useful data mining technique that allows you to use your past data instances, with known output values, to predict an unknown output value of a new data instance. So, at this point, this description should sound ...
... Nearest Neighbor (also known as Collaborative Filtering or Instance-based Learning) is a useful data mining technique that allows you to use your past data instances, with known output values, to predict an unknown output value of a new data instance. So, at this point, this description should sound ...
Hands On Lab: Building Your First Data Mining Model with SQL
... In this task, you will process the Basket Analysis mining model. Once processed, the mining model will contain the patterns and statistics that describe the relationships between frequently purchased models. 1. In Solution Explorer, inside the Sales Analysis project, right-click the BasketAnalysis m ...
... In this task, you will process the Basket Analysis mining model. Once processed, the mining model will contain the patterns and statistics that describe the relationships between frequently purchased models. 1. In Solution Explorer, inside the Sales Analysis project, right-click the BasketAnalysis m ...
Chapter 23 Mining for Complex Models Comprising Feature
... Let X = {xi : i = 1, . . . , m} be a set of input patterns and Y = {yi : i = 1, . . . , m} a set of class labels. The final class label (decision) on unseen vector x is computed as a conditional probability of class c given vector x: P (c|x, X , Y ) = ...
... Let X = {xi : i = 1, . . . , m} be a set of input patterns and Y = {yi : i = 1, . . . , m} a set of class labels. The final class label (decision) on unseen vector x is computed as a conditional probability of class c given vector x: P (c|x, X , Y ) = ...