Slides 17: Waiting for Disaster (PDF, 135 KB)
... Survival Analysis We will use a few concepts from survival analysis. Survival analysis models waiting for an event to happen. It was first developed in medicine, where it was used to model how long terminally ill patients had to live. The associated terminology is grim. We will use it to model how ...
... Survival Analysis We will use a few concepts from survival analysis. Survival analysis models waiting for an event to happen. It was first developed in medicine, where it was used to model how long terminally ill patients had to live. The associated terminology is grim. We will use it to model how ...
SPECTR1
... Employing automated logistics modeling techniques offers an opportunity for conserving resources by facilitating optimal integrated logistics support decisions. Danger exists in suboptimizing based only on immediate visible constraints. An example of such an incorrect suboptimization occurs when one ...
... Employing automated logistics modeling techniques offers an opportunity for conserving resources by facilitating optimal integrated logistics support decisions. Danger exists in suboptimizing based only on immediate visible constraints. An example of such an incorrect suboptimization occurs when one ...
Bayesian Mixed Membership Models for Soft Clustering and
... groups, where the nature, size, and often the number of the groups is unspecified in advance. The classification problem has a similar form except that the nature and the number of groups are either known theoretically or inferred from units in a training data set with known group assignments. In ma ...
... groups, where the nature, size, and often the number of the groups is unspecified in advance. The classification problem has a similar form except that the nature and the number of groups are either known theoretically or inferred from units in a training data set with known group assignments. In ma ...
Slide 1
... Originally proposed for decision tree learning It builds a series of models with increasingly larger samples until accuracy no longer improves The sample sizes follow a sample schedule S = {n1, n2, …, nk } where ni is sample size for the i-th model Geometric sampling schedule is efficient in ...
... Originally proposed for decision tree learning It builds a series of models with increasingly larger samples until accuracy no longer improves The sample sizes follow a sample schedule S = {n1, n2, …, nk } where ni is sample size for the i-th model Geometric sampling schedule is efficient in ...
Customer Segmentation based on Neural Network with Clustering
... information is transmitted in the network [5]. Since BP neural network is the most widely used architecture, we adopt BP neural network in this research[6]. All layers use sigmoid function as the transfer function. Since it is difficult for the network to output extreme values (i.e., near 0 and 1), ...
... information is transmitted in the network [5]. Since BP neural network is the most widely used architecture, we adopt BP neural network in this research[6]. All layers use sigmoid function as the transfer function. Since it is difficult for the network to output extreme values (i.e., near 0 and 1), ...
No Slide Title - School of Computing
... • Understand a bit more about the merits of pie charts and bar graphs • Create a dataset with roughly equal numbers in each class • Which is best if the task is to discriminate? ...
... • Understand a bit more about the merits of pie charts and bar graphs • Create a dataset with roughly equal numbers in each class • Which is best if the task is to discriminate? ...
Logistic Regression
... For example, the odds of CHD are multiplied by the factor exp(0.0243) = 1.025 for every increase of 1 mmHg in SBP. A difference of 10 mmHg multiplies the odds of CHD by (1.025)10, or ...
... For example, the odds of CHD are multiplied by the factor exp(0.0243) = 1.025 for every increase of 1 mmHg in SBP. A difference of 10 mmHg multiplies the odds of CHD by (1.025)10, or ...
Big Data - IOE Notes
... properties (such as tumors found in medical images). Association algorithms find correlations between different attributes in a data set (such as the automatically suggested search terms in response to a query). Sequence analysis algorithms summarize frequent sequences in data (such as understanding ...
... properties (such as tumors found in medical images). Association algorithms find correlations between different attributes in a data set (such as the automatically suggested search terms in response to a query). Sequence analysis algorithms summarize frequent sequences in data (such as understanding ...
new method of variable selection for binary data cluster analysis
... sets are too big for agglomerative algorithms. The most popular and applied in virtually any comparative simulation study is the k-means clustering. However, in the case of binary data sets, it cannot be used under no pretence whatever. Firstly, as in the case of the methods described in section 2, ...
... sets are too big for agglomerative algorithms. The most popular and applied in virtually any comparative simulation study is the k-means clustering. However, in the case of binary data sets, it cannot be used under no pretence whatever. Firstly, as in the case of the methods described in section 2, ...
Hysol MB 50 Coolant
... 100% of the plant to Hysol MB 50. The customer recognizes the benefits of the product’s performance and now understands the impact of the coolant on total operating costs. ...
... 100% of the plant to Hysol MB 50. The customer recognizes the benefits of the product’s performance and now understands the impact of the coolant on total operating costs. ...
Rendering scenes
... Models are stored in flat files With thanks to Andrew Borley, Jonathan Davies, Neil Hardman and George Hicken of IBM UK. ...
... Models are stored in flat files With thanks to Andrew Borley, Jonathan Davies, Neil Hardman and George Hicken of IBM UK. ...
GUIDELINES FOR BASIC STATISTICS
... your prediction, but not being directly tested. Assumptions are really a part of the logical argument you are building when constructing a hypothesis/prediction. Once you have a quantitative prediction firmly in mind for your experiment or study system, you can proceed to the next step – forming sta ...
... your prediction, but not being directly tested. Assumptions are really a part of the logical argument you are building when constructing a hypothesis/prediction. Once you have a quantitative prediction firmly in mind for your experiment or study system, you can proceed to the next step – forming sta ...
GUIDELINES FOR BASIC STATISTICS(Va)
... your prediction, but not being directly tested. Assumptions are really a part of the logical argument you are building when constructing a hypothesis/prediction. Once you have a quantitative prediction firmly in mind for your experiment or study system, you can proceed to the next step – forming sta ...
... your prediction, but not being directly tested. Assumptions are really a part of the logical argument you are building when constructing a hypothesis/prediction. Once you have a quantitative prediction firmly in mind for your experiment or study system, you can proceed to the next step – forming sta ...
Performance Controlled Data Reduction for Knowledge Discovery in
... bound on the size of a data set needed for an accurate and efficient analysis. For example, for many applications increasing the data set size 10 times for a possible accuracy gain of 1% can not justify huge additional computational costs. Also, overly large training data sets can result in increasi ...
... bound on the size of a data set needed for an accurate and efficient analysis. For example, for many applications increasing the data set size 10 times for a possible accuracy gain of 1% can not justify huge additional computational costs. Also, overly large training data sets can result in increasi ...
Web analytics and advertising
... • We began working with the consulting agency Nordic eMarketing in the spring of 2007 • IndexTools chosen as our analytics tool • Implementation began summer 2007 • The use of the tool is an ongoing project ...
... • We began working with the consulting agency Nordic eMarketing in the spring of 2007 • IndexTools chosen as our analytics tool • Implementation began summer 2007 • The use of the tool is an ongoing project ...
WOOD 492 MODELLING FOR DECISION SUPPORT
... • quantitative representation of a system, showing the inter-relationships of its different components • Is used to show the essence of a business/economic problem • A mathematical model has 4 components: ...
... • quantitative representation of a system, showing the inter-relationships of its different components • Is used to show the essence of a business/economic problem • A mathematical model has 4 components: ...
Marketing Models: Past, Present and Future
... The purpose of stylized theoretical models in marketing is to explain stylized marketing phenomena: A stylized theoretical model makes a set of assumptions that describes a hypothesized marketing environment. Some of these assumptions will be purely mathematical, designed to make the analysis tracta ...
... The purpose of stylized theoretical models in marketing is to explain stylized marketing phenomena: A stylized theoretical model makes a set of assumptions that describes a hypothesized marketing environment. Some of these assumptions will be purely mathematical, designed to make the analysis tracta ...
Controlling Medical Severity - Oregon Self
... • Improve patient health and safety • Increased productivity • Eliminate fraud and inappropriate utilization patterns ...
... • Improve patient health and safety • Increased productivity • Eliminate fraud and inappropriate utilization patterns ...
here
... Description: Power grids may be the largest and most complex M2M network systems on the globe. IoT technologies are changing the operation and management paradigm of the grid. In addition to SCADA, Distribution Management Systems, Outage Management Systems (OMS), and Advanced Metering Infrastructure ...
... Description: Power grids may be the largest and most complex M2M network systems on the globe. IoT technologies are changing the operation and management paradigm of the grid. In addition to SCADA, Distribution Management Systems, Outage Management Systems (OMS), and Advanced Metering Infrastructure ...
“Counting Your Customers” One by One:
... correlation between log(λ) and log(μ) allows a formal testing of the independence assumption in a Pareto/NBD model. 7. Ease of model extension. Hierarchical models, whereby customer-specific parameters are a function of covariates, can be constructed and estimated with ease. (a) Schmittlein and Pete ...
... correlation between log(λ) and log(μ) allows a formal testing of the independence assumption in a Pareto/NBD model. 7. Ease of model extension. Hierarchical models, whereby customer-specific parameters are a function of covariates, can be constructed and estimated with ease. (a) Schmittlein and Pete ...
#R code: Discussion 6
... Model2 = lm( Hours ~ Holiday+Cases+Costs, data=Data) anova(Model2) #to get SSR( X2, X3 | X1 ) = SSE( X1 ) – SSE( X1, X2, X3 ), #use equation (7.4b). You need: #run a linear model involving only X1 to obtain SSE( X1 ). Model3 = lm( Hours ~ Cases, data=Data) SSE.x1 = anova(Model3)[1,3] #then calculate ...
... Model2 = lm( Hours ~ Holiday+Cases+Costs, data=Data) anova(Model2) #to get SSR( X2, X3 | X1 ) = SSE( X1 ) – SSE( X1, X2, X3 ), #use equation (7.4b). You need: #run a linear model involving only X1 to obtain SSE( X1 ). Model3 = lm( Hours ~ Cases, data=Data) SSE.x1 = anova(Model3)[1,3] #then calculate ...
No Slide Title
... customer bases, and then use this knowledge to develop targeted marketing programs Land use: Identification of areas of similar land use in an earth observation database Insurance: Identifying groups of motor insurance policy holders with a high average claim cost City-planning: Identifying groups o ...
... customer bases, and then use this knowledge to develop targeted marketing programs Land use: Identification of areas of similar land use in an earth observation database Insurance: Identifying groups of motor insurance policy holders with a high average claim cost City-planning: Identifying groups o ...
Churn Analysis Case Study
... or very little profitable. When this kind of knowledge is available to a company, marketing managers may take informed and strategic action to minimize defections, win back valued defectors, and attract more cost-effectively the right kind of customers in the future – including those that are least ...
... or very little profitable. When this kind of knowledge is available to a company, marketing managers may take informed and strategic action to minimize defections, win back valued defectors, and attract more cost-effectively the right kind of customers in the future – including those that are least ...
data empowerment developing data strategies and tactics for
... Every good marketing plan today begins with collecting relevant data. The problem is most small to medium organizations find this a challenge they can’t handle. There is no one real location for the customer to find all their data. Most often data is siloed in many different locations. It can be fou ...
... Every good marketing plan today begins with collecting relevant data. The problem is most small to medium organizations find this a challenge they can’t handle. There is no one real location for the customer to find all their data. Most often data is siloed in many different locations. It can be fou ...
Using data mining to detect insurance fraud
... insights into current performance and the ability to predict future outcomes. Combined with rich industry solutions, proven practices and professional services, organizations of every size can drive the highest productivity, confidently automate decisions and deliver better results. As part of this ...
... insights into current performance and the ability to predict future outcomes. Combined with rich industry solutions, proven practices and professional services, organizations of every size can drive the highest productivity, confidently automate decisions and deliver better results. As part of this ...