material - Dr. Fei Hu
... the input variables in the feature vector. Each node corresponds to one of the feature vector variables. From every node there are edges to children, where there is an edge per each of the possible values (or range of values) of the input variable associated with the node. Each leaf represents a pos ...
... the input variables in the feature vector. Each node corresponds to one of the feature vector variables. From every node there are edges to children, where there is an edge per each of the possible values (or range of values) of the input variable associated with the node. Each leaf represents a pos ...
Using Artificial Neural Network to Predict Collisions on Horizontal
... the ANN models have the lowest mean square error value than those of the statistical models. Similarly, the AIC values of the ANN models are smaller to those of the regression models for all the combinations. Consequently, the ANN models have better statistical ...
... the ANN models have the lowest mean square error value than those of the statistical models. Similarly, the AIC values of the ANN models are smaller to those of the regression models for all the combinations. Consequently, the ANN models have better statistical ...
Title A Multi-Agent System for Context
... A. Towards Context-based Distributed Data Mining In statistical meta-analysis, a popular way to model unobservable or immeasurable context heterogeneity is to assume that the heterogeneity across different sites is random. In other words, context heterogeneity derives from essentially random differe ...
... A. Towards Context-based Distributed Data Mining In statistical meta-analysis, a popular way to model unobservable or immeasurable context heterogeneity is to assume that the heterogeneity across different sites is random. In other words, context heterogeneity derives from essentially random differe ...
Research on a simplified variable analysis of credit rating in
... with the degree of cyclical factors. The Credit Monitor model was developed by KMV Ltd. in the United States, and the method estimated the probability of loan defaults. The model Credit Risk + was issued by the financial products development department in the Swiss Credit Bank, which was the model t ...
... with the degree of cyclical factors. The Credit Monitor model was developed by KMV Ltd. in the United States, and the method estimated the probability of loan defaults. The model Credit Risk + was issued by the financial products development department in the Swiss Credit Bank, which was the model t ...
One-class to multi-class model update using the class
... AI Researcher Symposium (STAIRS). The papers from PAIS are included in this volume, while the papers from STAIRS are published in a separate volume. ECAI 2016 also featured a special topic on Artificial Intelligence for Human Values, with a dedicated track and a public event in the Peace Palace in T ...
... AI Researcher Symposium (STAIRS). The papers from PAIS are included in this volume, while the papers from STAIRS are published in a separate volume. ECAI 2016 also featured a special topic on Artificial Intelligence for Human Values, with a dedicated track and a public event in the Peace Palace in T ...
PPT
... Ex. An e-game could belong to both entertainment and software Methods: fuzzy clusters and probabilistic model-based clusters Fuzzy cluster: A fuzzy set S: FS : X → [0, 1] (value between 0 and 1) Example: Popularity of cameras is defined as a fuzzy mapping ...
... Ex. An e-game could belong to both entertainment and software Methods: fuzzy clusters and probabilistic model-based clusters Fuzzy cluster: A fuzzy set S: FS : X → [0, 1] (value between 0 and 1) Example: Popularity of cameras is defined as a fuzzy mapping ...
Towards common-sense reasoning via conditional
... capture the essence of supervised, unsupervised, and reinforcement learning, each major areas in modern AI.1 In Sections 5 and 7 we will return to Turing’s writings on these matters. One major area of Turing’s contributions, while often overlooked, is statistics. In fact, Turing, along with I. J. Go ...
... capture the essence of supervised, unsupervised, and reinforcement learning, each major areas in modern AI.1 In Sections 5 and 7 we will return to Turing’s writings on these matters. One major area of Turing’s contributions, while often overlooked, is statistics. In fact, Turing, along with I. J. Go ...
Detecting Statistical Interactions with Additive Groves of Trees
... between important variables, we need to build a restricted model that uses these variables in different additive components of the function. There is a class of ensembles that allows us to do this: additive models. Each component in an additive model is trained on the residuals of predictions of all ...
... between important variables, we need to build a restricted model that uses these variables in different additive components of the function. There is a class of ensembles that allows us to do this: additive models. Each component in an additive model is trained on the residuals of predictions of all ...
Dropout as a Bayesian Approximation: Representing Model
... Standard deep learning tools for regression and classification do not capture model uncertainty. In classification, predictive probabilities obtained at the end of the pipeline (the softmax output) are often erroneously interpreted as model confidence. A model can be uncertain in its predictions eve ...
... Standard deep learning tools for regression and classification do not capture model uncertainty. In classification, predictive probabilities obtained at the end of the pipeline (the softmax output) are often erroneously interpreted as model confidence. A model can be uncertain in its predictions eve ...
A Summarizing Data Succinctly with the Most Informative Itemsets
... and in turn we update our model accordingly. As we use the Maximum Entropy principle to obtain unbiased probabilistic models, and only include those itemsets that are most informative with regard to the current model, the summaries we construct are guaranteed to be both descriptive and non-redundant ...
... and in turn we update our model accordingly. As we use the Maximum Entropy principle to obtain unbiased probabilistic models, and only include those itemsets that are most informative with regard to the current model, the summaries we construct are guaranteed to be both descriptive and non-redundant ...
Incremental Ensemble Learning for Electricity Load Forecasting
... learning, the ensemble is formed by models of the same type that are learned on different subsets of available data. The heterogeneous learning process applies different types of models. The combination of homogeneous and heterogeneous approaches was also presented in the literature. The best known ...
... learning, the ensemble is formed by models of the same type that are learned on different subsets of available data. The heterogeneous learning process applies different types of models. The combination of homogeneous and heterogeneous approaches was also presented in the literature. The best known ...
Bounded Rationality in Randomization
... I need a threshold number of paths on which to calculate rank correlations before predictions are practical. Let w̃ be this parameter. It is fixed at l + 1, its theoretical minimum. This choice also biases against finding significance because correlations will be calculated even when there is only o ...
... I need a threshold number of paths on which to calculate rank correlations before predictions are practical. Let w̃ be this parameter. It is fixed at l + 1, its theoretical minimum. This choice also biases against finding significance because correlations will be calculated even when there is only o ...
PDF - Tuan Anh Le
... the generative model, within the structural regularization framework of a parameterized non-linear transformation of the latent variables. Approaches in this camp generally produce recognition networks that nonlinearly transform observational data at test time into parameters of a variational poster ...
... the generative model, within the structural regularization framework of a parameterized non-linear transformation of the latent variables. Approaches in this camp generally produce recognition networks that nonlinearly transform observational data at test time into parameters of a variational poster ...
Decision Trees Based Image Data Mining and Its Application on
... In this section, the kernel of the proposed model including two phases will be discussed. These two phases are: image transformation and image mining. (1) Image Transformation Phase: This relates to how to transform input images into database-like tables and encode the related features. (2) Image Mi ...
... In this section, the kernel of the proposed model including two phases will be discussed. These two phases are: image transformation and image mining. (1) Image Transformation Phase: This relates to how to transform input images into database-like tables and encode the related features. (2) Image Mi ...
Scaling Clustering Algorithms to Large Databases
... this phase on past data samples. The second primary compression method (PDC2) creates a “worst case scenario” by perturbing the cluster means within computed confidence intervals. For each data point in the buffer, perturb the K estimated cluster means within their respective confidence intervals so ...
... this phase on past data samples. The second primary compression method (PDC2) creates a “worst case scenario” by perturbing the cluster means within computed confidence intervals. For each data point in the buffer, perturb the K estimated cluster means within their respective confidence intervals so ...
Scaling Clustering Algorithms to Large Databases
... this phase on past data samples. The second primary compression method (PDC2) creates a “worst case scenario” by perturbing the cluster means within computed confidence intervals. For each data point in the buffer, perturb the K estimated cluster means within their respective confidence intervals so ...
... this phase on past data samples. The second primary compression method (PDC2) creates a “worst case scenario” by perturbing the cluster means within computed confidence intervals. For each data point in the buffer, perturb the K estimated cluster means within their respective confidence intervals so ...
Indexing Density Models for Incremental Learning and Anytime
... Another approach to density estimation are kernel densities, which do not make any assumption about the underlying data distribution (thus often termed “model-free” or “non-parameterized” density-estimation). Kernel estimators can be seen as influence functions centered at each data object. To smoot ...
... Another approach to density estimation are kernel densities, which do not make any assumption about the underlying data distribution (thus often termed “model-free” or “non-parameterized” density-estimation). Kernel estimators can be seen as influence functions centered at each data object. To smoot ...