curriculum vitae jeffrey m. wooldridge
... Editor, Journal of Business and Economic Statistics, 1998 to 2000. Co-Editor, Economics Letters, 1995 to 1998. Associate Editor, Stata Journal, 2002 to present. Associate Editor, Journal of Business and Economic Statistics, 1995 to 1997. Associate Editor, Journal of Econometrics, 1995 to 1997; 2001 ...
... Editor, Journal of Business and Economic Statistics, 1998 to 2000. Co-Editor, Economics Letters, 1995 to 1998. Associate Editor, Stata Journal, 2002 to present. Associate Editor, Journal of Business and Economic Statistics, 1995 to 1997. Associate Editor, Journal of Econometrics, 1995 to 1997; 2001 ...
On the use of Side Information for Mining Text Data
... attributes. Even in this case, the derived binary attributes are quite sparse especially when the numerical ranges are discretized into a large number of attributes. In the case of categorical data, we can define a binary attribute for each possible categorical value. In many cases, the number of suc ...
... attributes. Even in this case, the derived binary attributes are quite sparse especially when the numerical ranges are discretized into a large number of attributes. In the case of categorical data, we can define a binary attribute for each possible categorical value. In many cases, the number of suc ...
A Review on Ensembles for the Class Imbalance Problem: Bagging
... is necessary [36]–[38] and there exist several different ways to achieve it [39]. In this paper, we focus on data variation-based ensembles, which consist in the manipulation of the training examples in such a way that each classifier is trained with a different training set. AdaBoost [40], [41] and ...
... is necessary [36]–[38] and there exist several different ways to achieve it [39]. In this paper, we focus on data variation-based ensembles, which consist in the manipulation of the training examples in such a way that each classifier is trained with a different training set. AdaBoost [40], [41] and ...
Improving the Accuracy of Decision Tree Induction by - IBaI
... feature subset selection problem, a learning algorithm is faced with the problem of selecting some subset of features upon which to focus its attention. In this paper, we present our study on features subset selection and classification with the C4.5 algorithm. In Section 2, we briefly describe the ...
... feature subset selection problem, a learning algorithm is faced with the problem of selecting some subset of features upon which to focus its attention. In this paper, we present our study on features subset selection and classification with the C4.5 algorithm. In Section 2, we briefly describe the ...
3 Supervised Learning
... same class as the original class of the test case. There are also other measures that can be used. We will discuss them in Sect. 3.3. We pause here to raises two important questions: 1. What do we mean by learning by a computer system? 2. What is the relationship between the training and the test da ...
... same class as the original class of the test case. There are also other measures that can be used. We will discuss them in Sect. 3.3. We pause here to raises two important questions: 1. What do we mean by learning by a computer system? 2. What is the relationship between the training and the test da ...
GLM: Multiple Predictor Variables
... You can see how these SS are “hierarchical” and “sequential.” The Type III SS for any variable are computed by controlling for all other variables in the model. Hence, the SS for A are calculated after the effects of B and C are removed from the data. Those for B are computed using the residuals afte ...
... You can see how these SS are “hierarchical” and “sequential.” The Type III SS for any variable are computed by controlling for all other variables in the model. Hence, the SS for A are calculated after the effects of B and C are removed from the data. Those for B are computed using the residuals afte ...
A novel algorithm for fast and scalable subspace clustering of high
... knowledge helps to prune the non-dense neighbourhoods of the data points in the lower dimensional subspaces as they will never lead to the dense neighbourhoods in the higher dimensional subspaces. Thus, only the dense set of points (clusters) starting from the 1-dimensional subspaces, are chosen as ...
... knowledge helps to prune the non-dense neighbourhoods of the data points in the lower dimensional subspaces as they will never lead to the dense neighbourhoods in the higher dimensional subspaces. Thus, only the dense set of points (clusters) starting from the 1-dimensional subspaces, are chosen as ...
Chapter 9 Part 1
... HD algorithm starts by considering all divisions of the data into two nonempty subsets n 1 which amounts to possibilities. ...
... HD algorithm starts by considering all divisions of the data into two nonempty subsets n 1 which amounts to possibilities. ...
Numerical
... 1.2 Bisection Method Let us suppose we have an equation of the form f(x) = 0 in which solution lies between in the range (a,b). Also f(x) is continuous and it can be algebraic or transcendental. If f(a) and f(b) are opposite signs, then there exist atleast one real root between a and b. Let f(a) be ...
... 1.2 Bisection Method Let us suppose we have an equation of the form f(x) = 0 in which solution lies between in the range (a,b). Also f(x) is continuous and it can be algebraic or transcendental. If f(a) and f(b) are opposite signs, then there exist atleast one real root between a and b. Let f(a) be ...
Expectation–maximization algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.