Does Query-Based Diagnostics Work?
... context information, observations, and the final diagnosis. While, in case of the computing lab help desk data, we had full knowledge of the three types of information, we did not know which of the features in the Irvine medical data sets were context variables and which were observations. Effective ...
... context information, observations, and the final diagnosis. While, in case of the computing lab help desk data, we had full knowledge of the three types of information, we did not know which of the features in the Irvine medical data sets were context variables and which were observations. Effective ...
Learning Markov Networks With Arithmetic Circuits
... their given values and 0 otherwise. Unlike previous work, there are neither latent variables nor explicit restrictions on the treewidth or structure of these features, as long as they admit a model with efficient inference. To ensure efficient inference, ACMN simultaneously learns an arithmetic circ ...
... their given values and 0 otherwise. Unlike previous work, there are neither latent variables nor explicit restrictions on the treewidth or structure of these features, as long as they admit a model with efficient inference. To ensure efficient inference, ACMN simultaneously learns an arithmetic circ ...
The 1995 Cognitive Science Conference Paper Submission Format
... constraint satisfaction connectionist models can account for four principles of coherence that underlie social explanation. However, it is argued that the specific implementation they employed (ECHO; Thagard, 1992) has several important shortcomings. ECHO fails to be sensitive to covariation which i ...
... constraint satisfaction connectionist models can account for four principles of coherence that underlie social explanation. However, it is argued that the specific implementation they employed (ECHO; Thagard, 1992) has several important shortcomings. ECHO fails to be sensitive to covariation which i ...
Correlation v
... Correlation v. Causal relationships: What are scientists looking for? Psychology is the study of human behavior, but studying human behavior can be tricky. Ideally, scientists discover a causal relationship between variables, but often times, they can only identify a correlation. First, the terms: C ...
... Correlation v. Causal relationships: What are scientists looking for? Psychology is the study of human behavior, but studying human behavior can be tricky. Ideally, scientists discover a causal relationship between variables, but often times, they can only identify a correlation. First, the terms: C ...
Generalized Information Requirements of Intelligent Decision-Making Systems
... user also supplies a d¥namic model, f, which describes reality at tlme t+l-a5ia function of reality at time t and actions (~(t)) at time t. Dynamic programming then translates this long-term optimization problem into a short-term optimization problem, which is more tractable. It calculates the secon ...
... user also supplies a d¥namic model, f, which describes reality at tlme t+l-a5ia function of reality at time t and actions (~(t)) at time t. Dynamic programming then translates this long-term optimization problem into a short-term optimization problem, which is more tractable. It calculates the secon ...
Using Bayesian Networks and Simulation for Data
... Intelligence (AI) and have proven successful in “intelligent” applications such as medical expert systems, speech recognition, and fault diagnosis. In practical terms, one of the major benefits from using BNs is in that probabilistic and causal relationships among variables are represented and execu ...
... Intelligence (AI) and have proven successful in “intelligent” applications such as medical expert systems, speech recognition, and fault diagnosis. In practical terms, one of the major benefits from using BNs is in that probabilistic and causal relationships among variables are represented and execu ...
Week11 - Information Management and Systems
... automatically building models, and correcting over and over again the model’s own mistakes – (Dhar and Stein, 1997) Good at modelling poorly understood problems for which sufficient data can be collected ...
... automatically building models, and correcting over and over again the model’s own mistakes – (Dhar and Stein, 1997) Good at modelling poorly understood problems for which sufficient data can be collected ...
Knowledge-Driven Business Intelligence Systems: Part II
... that the consequent fuzzy regions are overloaded > system loses the information provided by the fuzzy rules Needs domain expertise to setup fuzzy sets Only provides approximation to human reasoning ...
... that the consequent fuzzy regions are overloaded > system loses the information provided by the fuzzy rules Needs domain expertise to setup fuzzy sets Only provides approximation to human reasoning ...
DATA MINING IN FINANCE AND ACCOUNTING: A - delab-auth
... An important disadvantage of NNs is that they act as black boxes as it is difficult to humans to interpret the way NNs reach their decisions. However, algorithms have been proposed to extract comprehendible rules from NNs. Another criticism on NNs is that a number of parameters like the network topo ...
... An important disadvantage of NNs is that they act as black boxes as it is difficult to humans to interpret the way NNs reach their decisions. However, algorithms have been proposed to extract comprehendible rules from NNs. Another criticism on NNs is that a number of parameters like the network topo ...
Grammatical Bigrams - Stanford Artificial Intelligence Laboratory
... link precision in this setting is 80.6%. II. Generalization. In this experiment, we measure the model’s ability to generalize from labelled data. The model is trained on Ltrain and then tested on Ltest . The model’s link precision in this setting is 61.8%. III. Induction. In this experiment, we meas ...
... link precision in this setting is 80.6%. II. Generalization. In this experiment, we measure the model’s ability to generalize from labelled data. The model is trained on Ltrain and then tested on Ltest . The model’s link precision in this setting is 61.8%. III. Induction. In this experiment, we meas ...
ppt - TAMU Computer Science Faculty Pages
... Least square and robust estimator (initialization) treat inliers and outliers equally, as a whole. Robust estimator tries to extract the outliers in the later iteration, while fitting inliers and extracting outliers should be in the same process. Why not randomly choose data subset to fit – RANSAC. ...
... Least square and robust estimator (initialization) treat inliers and outliers equally, as a whole. Robust estimator tries to extract the outliers in the later iteration, while fitting inliers and extracting outliers should be in the same process. Why not randomly choose data subset to fit – RANSAC. ...
Modeling Student Learning: Binary or Continuous Skill?
... binary latent variable (either learned or unlearned). Figure 1 illustrates the model; the illustration is done in a nonstandard way to stress the relation of the model to the model with continuous skill. The estimated skill is updated using a Bayes rule based on the observed answers; the prediction ...
... binary latent variable (either learned or unlearned). Figure 1 illustrates the model; the illustration is done in a nonstandard way to stress the relation of the model to the model with continuous skill. The estimated skill is updated using a Bayes rule based on the observed answers; the prediction ...
Advances in Environmental Biology Systems
... In today‟s manufacturing industry, due to high equipment cost and capacity limitations, different products with their own recipes are being processed on various types of tools [19]. This brings an extremely complex operation condition to the tools and makes the equipment degradation highly unpredict ...
... In today‟s manufacturing industry, due to high equipment cost and capacity limitations, different products with their own recipes are being processed on various types of tools [19]. This brings an extremely complex operation condition to the tools and makes the equipment degradation highly unpredict ...
Grammatical Bigrams
... link precision in this setting is 80.6%. II. Generalization. In this experiment, we measure the model's ability to generalize from labelled data. The model is trained on Ltrain and then tested on Ltest. The model's link precision in this setting is 61.8%. III. Induction. In this experiment, we measu ...
... link precision in this setting is 80.6%. II. Generalization. In this experiment, we measure the model's ability to generalize from labelled data. The model is trained on Ltrain and then tested on Ltest. The model's link precision in this setting is 61.8%. III. Induction. In this experiment, we measu ...
Multi-Conditional Learning: Generative/Discriminative Training for
... a globally normalized product of local functions. In our experiments here we shall use the harmonium’s factorization structure to define an MRF and we will then define sets of marginal conditionals distributions of some observed variables given others that are of particular interest so as to form ou ...
... a globally normalized product of local functions. In our experiments here we shall use the harmonium’s factorization structure to define an MRF and we will then define sets of marginal conditionals distributions of some observed variables given others that are of particular interest so as to form ou ...
Artificial General Intelligence through Large
... course, probabilistic inference can be very computationally expensive (a naive approach to answering a query would involve summing or integrating over all the unobserved, non-query variables). We have no silver bullet for this problem, but there are indications that performing inference on an AGI mo ...
... course, probabilistic inference can be very computationally expensive (a naive approach to answering a query would involve summing or integrating over all the unobserved, non-query variables). We have no silver bullet for this problem, but there are indications that performing inference on an AGI mo ...
A Case Study: Improve Classification of Rare Events
... The Neural Network Model has a higher ROC Index, but it shows the same value as before, so does its misclassification rate. If we look closer we can find that the model’s sensitivity has increased slightly as can be seen in Figure 2, while the area under the curve remain the same. ...
... The Neural Network Model has a higher ROC Index, but it shows the same value as before, so does its misclassification rate. If we look closer we can find that the model’s sensitivity has increased slightly as can be seen in Figure 2, while the area under the curve remain the same. ...
OBDD-Based Planning with Real-Valued Variables in Non-Deterministic Environments
... variables are handled by requiring domains to either represent real variables as relative booleans (e.g. using ontable or on-block in the classical blocks world), or to explicitly enumerate each possible value for a real variable (e.g. using at11, at12, at21, at22 for block position in a 2x2 blocks ...
... variables are handled by requiring domains to either represent real variables as relative booleans (e.g. using ontable or on-block in the classical blocks world), or to explicitly enumerate each possible value for a real variable (e.g. using at11, at12, at21, at22 for block position in a 2x2 blocks ...
Learning from learning curves: Item Response Theory
... A rank ordering of most predictive cognitive models For each model, a measure of its generalizability & parameter estimates for knowledge component difficulty, learning rates, & ...
... A rank ordering of most predictive cognitive models For each model, a measure of its generalizability & parameter estimates for knowledge component difficulty, learning rates, & ...
Three Approaches to Probability Model Selection
... where Pl· ... , Pm are positive numbers summing to one and f1 (x), ... , fm(x) are the component densities. Mixtures of analytically tractable component distributions, such as Gaussians, are useful to model not only true mixtures but any continuous probability distributions with which fast calculati ...
... where Pl· ... , Pm are positive numbers summing to one and f1 (x), ... , fm(x) are the component densities. Mixtures of analytically tractable component distributions, such as Gaussians, are useful to model not only true mixtures but any continuous probability distributions with which fast calculati ...
ItemResponseTheory - Carnegie Mellon School of Computer
... A rank ordering of most predictive cognitive models For each model, a measure of its generalizability & parameter estimates for knowledge component difficulty, learning rates, & ...
... A rank ordering of most predictive cognitive models For each model, a measure of its generalizability & parameter estimates for knowledge component difficulty, learning rates, & ...
Artificial Intelligence Support for Scientific Model
... phenomenondevelop theories in order to account for novel observations and to make predictions about expected behavior. To validate their theories, scientists conduct in situ experiments whenever possible. Often, however,it is not possible to carry out direct experiments due to cost or other limiting ...
... phenomenondevelop theories in order to account for novel observations and to make predictions about expected behavior. To validate their theories, scientists conduct in situ experiments whenever possible. Often, however,it is not possible to carry out direct experiments due to cost or other limiting ...
An introduction to graphical models
... engineering – uncertainty and complexity – and in particular they are playing an increasingly important role in the design and analysis of machine learning algorithms. Fundamental to the idea of a graphical model is the notion of modularity – a complex system is built by combining simpler parts. Pro ...
... engineering – uncertainty and complexity – and in particular they are playing an increasingly important role in the design and analysis of machine learning algorithms. Fundamental to the idea of a graphical model is the notion of modularity – a complex system is built by combining simpler parts. Pro ...
Graphical Causal Models: A Short Annotated Bibliography
... Tetrad 3 is an outofdate version of the Tetrad software but contains an accessible discussion of the methods, there basis, and applications. It can be downloaded from the Tetrad Project website: http://www.phil.cmu.edu/projects/tetrad/index.html. · Hoover, Kevin D. (2005) ‘Automatic inference ...
... Tetrad 3 is an outofdate version of the Tetrad software but contains an accessible discussion of the methods, there basis, and applications. It can be downloaded from the Tetrad Project website: http://www.phil.cmu.edu/projects/tetrad/index.html. · Hoover, Kevin D. (2005) ‘Automatic inference ...
Preface to UMUAI Special Issue on Machine Learning for User
... actions that best satisfy the existing model, and selecting actions that best support refinement of the existing model. The remaining papers explore machine learning techniques that infer both the appropriate structure and parameters for a model. Sison et al use conceptual clustering to form bug des ...
... actions that best satisfy the existing model, and selecting actions that best support refinement of the existing model. The remaining papers explore machine learning techniques that infer both the appropriate structure and parameters for a model. Sison et al use conceptual clustering to form bug des ...
Structural equation modeling
Structural equation modeling (SEM) is a family of statistical methods designed to test a conceptual or theoretical model. Some common SEM methods include confirmatory factor analysis, path analysis, and latent growth modeling. The term ""structural equation model"" most commonly refers to a combination of two things: a ""measurement model"" that defines latent variables using one or more observed variables, and a ""structural regression model"" that links latent variables together. The parts of a structural equation model are linked to one another using a system of simultaneous regression equations.SEM is widely used in the social sciences because of its ability to isolate observational error from measurement of latent variables. To provide a simple example, the concept of human intelligence cannot be measured directly as one could measure height or weight. Instead, psychologists develop theories of intelligence and write measurement instruments with items (questions) designed to measure intelligence according to their theory. They would then use SEM to test their theory using data gathered from people who took their intelligence test. With SEM, ""intelligence"" would be the latent variable and the test items would be the observed variables.A simplistic model suggesting that intelligence (as measured by five questions) can predict academic performance (as measured by SAT, ACT, and high school GPA) is shown below. In SEM diagrams, latent variables are commonly shown as ovals and observed variables as rectangles. The below diagram shows how error (e) influences each intelligence question and the SAT, ACT, and GPA scores, but does not influence the latent variables. SEM provides numerical estimates for each of the parameters (arrows) in the model to indicate the strength of the relationships. Thus, in addition to testing the overall theory, SEM therefore allows the researcher to diagnose which observed variables are good indicators of the latent variables.Modern studies usually test much more specific models involving several theories, for example, Jansen, Scherer, and Schroeders (2015) studied how students' self-concept and self-efficacy affected educational outcomes. SEM is also used in the sciences, business, education, and many other fields.