Profiles in Innovation: Artificial Intelligence
... One of the more exciting aspects of the AI inflection is that “real-world” use cases abound. While deep-learning enabled advances in computer vision and such technologies as natural language processing are dramatically improving the quality of Apple’s Siri, Amazon’s Alexa, and Google’s photo recogni ...
... One of the more exciting aspects of the AI inflection is that “real-world” use cases abound. While deep-learning enabled advances in computer vision and such technologies as natural language processing are dramatically improving the quality of Apple’s Siri, Amazon’s Alexa, and Google’s photo recogni ...
Practical Data Analysis with JMP
... software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), D ...
... software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), D ...
Handling the Class Imbalance Problem in Binary Classification
... Natural processes often generate some observations more frequently than others. These processes result in an unbalanced distributions which cause the classifiers to bias toward the majority class especially because most classifiers assume a normal distribution. The quantity and the diversity of imba ...
... Natural processes often generate some observations more frequently than others. These processes result in an unbalanced distributions which cause the classifiers to bias toward the majority class especially because most classifiers assume a normal distribution. The quantity and the diversity of imba ...
On-line Error Analysis Using AI techniques A first sight
... Some researchers in fuzzy logic have explored the use of other interpretations of the AND and OR operations, but the definition for the NOT operation seems to be safe. If you plug just the values 0 and 1 into these definitions, you get the same truth tables as you would expect from conventional Bool ...
... Some researchers in fuzzy logic have explored the use of other interpretations of the AND and OR operations, but the definition for the NOT operation seems to be safe. If you plug just the values 0 and 1 into these definitions, you get the same truth tables as you would expect from conventional Bool ...
differential evolution based classification with pool of
... the process of finding the optimum of the optimization problem through evolving a population of candidate solutions. One of the most recently developed methods in evolutionary algorithm (EA) approaches is the differential evolution (DE) algorithm. The DE algorithm is a simple, stochastic, population ...
... the process of finding the optimum of the optimization problem through evolving a population of candidate solutions. One of the most recently developed methods in evolutionary algorithm (EA) approaches is the differential evolution (DE) algorithm. The DE algorithm is a simple, stochastic, population ...
Recursive partitioning and Bayesian inference
... several desirable properties that we establish for our prior in fact also hold for the Bayesian CART, most importantly posterior conjugacy and exactness. This implies that inference using Bayesian CART can often also be carried out in an exact manner by computing the posterior in closed-form. Up til ...
... several desirable properties that we establish for our prior in fact also hold for the Bayesian CART, most importantly posterior conjugacy and exactness. This implies that inference using Bayesian CART can often also be carried out in an exact manner by computing the posterior in closed-form. Up til ...
Reports on the Twenty-First National Conference on Artificial
... game-theoretic equilibrium models, which force them to make unrealistic assumptions such as homogeneity among agents and limit their analysis to small populations consisting of very few agents. However, recently, scientists have turned to building agentbased social simulation (ABSS) because it allow ...
... game-theoretic equilibrium models, which force them to make unrealistic assumptions such as homogeneity among agents and limit their analysis to small populations consisting of very few agents. However, recently, scientists have turned to building agentbased social simulation (ABSS) because it allow ...
INTELLIGENT DECISION-SUPPORT SYSTEM - Meta
... K. Sycara, Utility theory in conflict resolution P.S. Ow and S.F. Smith, Viewing scheduling as an opportunistic problem-solving process S. De, A knowledge-based approach to scheduling in an F.M.S. T.L. Dean, Reasoning about the effects of actions in automated planning systems D.P. Miller, A task and ...
... K. Sycara, Utility theory in conflict resolution P.S. Ow and S.F. Smith, Viewing scheduling as an opportunistic problem-solving process S. De, A knowledge-based approach to scheduling in an F.M.S. T.L. Dean, Reasoning about the effects of actions in automated planning systems D.P. Miller, A task and ...
12 Multiple Linear Regression
... regression coefficients. 5. Use the regression model to estimate the mean response, and to make predictions and to construct confidence intervals and prediction intervals. 6. Build regression models with polynomial terms. 7. Use indicator variables to model categorical regressors. 8. Use stepwise re ...
... regression coefficients. 5. Use the regression model to estimate the mean response, and to make predictions and to construct confidence intervals and prediction intervals. 6. Build regression models with polynomial terms. 7. Use indicator variables to model categorical regressors. 8. Use stepwise re ...
Probabilistic models for spike trains of single neurons
... the best possible manner 4. We found that while the Poisson and the mIMI models have an SS of 1 (precise for Poisson but to a first approximation for mIMI), the TRRP model has an SS that depends on the narrowness of the interspike interval (ISI) distribution 4.4. If the coefficient of variation (CV) ...
... the best possible manner 4. We found that while the Poisson and the mIMI models have an SS of 1 (precise for Poisson but to a first approximation for mIMI), the TRRP model has an SS that depends on the narrowness of the interspike interval (ISI) distribution 4.4. If the coefficient of variation (CV) ...
Time series
A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, intelligent transport and trajectory forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called ""time series analysis"", which focuses on comparing values of a single time series or multiple dependent time series at different points in time.Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility.)Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language.).