V34132136
... However, mostly the real life applications have transactions with quantitative values which are not binary. 1.1 Extraction of Interesting association patterns using Apriori The data to be processed by the DSS has to be free of noises, but it can exhibit uncertainty associated with it. As a result, t ...
... However, mostly the real life applications have transactions with quantitative values which are not binary. 1.1 Extraction of Interesting association patterns using Apriori The data to be processed by the DSS has to be free of noises, but it can exhibit uncertainty associated with it. As a result, t ...
MaxFlow.pdf
... has grown exponentially, it is often prohibitively time-consuming to solve these problems using traditional computing techniques, according to Jonathan Kelner, an associate professor of applied mathematics at MIT and a member of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). ...
... has grown exponentially, it is often prohibitively time-consuming to solve these problems using traditional computing techniques, according to Jonathan Kelner, an associate professor of applied mathematics at MIT and a member of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). ...
Chapter 11
... (a) Find the derivative of x3 using the definition of the derivative and using the power rule. Check that they agree. (b) If f 0 (2) = 4 and g 0 (2) = −1, what is (4f − 2g)0 (2)? (c) Find the equation of the tangent line to y = x3 − x + 1 at the point (2, 7). (d) Find the derivative of f (x) = (x2 − ...
... (a) Find the derivative of x3 using the definition of the derivative and using the power rule. Check that they agree. (b) If f 0 (2) = 4 and g 0 (2) = −1, what is (4f − 2g)0 (2)? (c) Find the equation of the tangent line to y = x3 − x + 1 at the point (2, 7). (d) Find the derivative of f (x) = (x2 − ...
speed review
... (B) the scores of students (out of 100 points) on a very easy exam in which most score perfectly, but a few do very poorly. (C) the prices of homes in a large city. (D) the scores of students (out of 100 points) on a very difficult exam on which most score poorly, but a few do very well. (E) the sal ...
... (B) the scores of students (out of 100 points) on a very easy exam in which most score perfectly, but a few do very poorly. (C) the prices of homes in a large city. (D) the scores of students (out of 100 points) on a very difficult exam on which most score poorly, but a few do very well. (E) the sal ...
LOYOLA COLLEGE (AUTONOMOUS), CHENNAI – 600 034
... 1. Why Data mining is so important? 2. Give formulae to determine chai square. 3. What are the two phases of implementation in clustering? 4. Why classification is not used in prediction? 5. What are the basic features of Clustering? 6. Mention the quality expected for clustering large databases. 7. ...
... 1. Why Data mining is so important? 2. Give formulae to determine chai square. 3. What are the two phases of implementation in clustering? 4. Why classification is not used in prediction? 5. What are the basic features of Clustering? 6. Mention the quality expected for clustering large databases. 7. ...
Maths-S1
... Understand the bell shaped curve and its link to probability Know how to calculate the value of z for any item of data in a normal distribution Use a positive z value to read a probability from the normal distribution table Use a negative z value to read a probability from the normal distribution ta ...
... Understand the bell shaped curve and its link to probability Know how to calculate the value of z for any item of data in a normal distribution Use a positive z value to read a probability from the normal distribution table Use a negative z value to read a probability from the normal distribution ta ...
Methods of Statistical Estimation
... Therefore, R2 is the portion of variation in y we can explain by x: For example, if c.c =.8 then R2 = :82 = :64 means that 64% of y¡variance may be explained by regressor. Properties of R2 : 1. R2 is nonnegative and less than 1. 2. If R2 = 1 then SS=0 and all points (xi ; yi ) lies on a straight lin ...
... Therefore, R2 is the portion of variation in y we can explain by x: For example, if c.c =.8 then R2 = :82 = :64 means that 64% of y¡variance may be explained by regressor. Properties of R2 : 1. R2 is nonnegative and less than 1. 2. If R2 = 1 then SS=0 and all points (xi ; yi ) lies on a straight lin ...
Estimate Sums
... Step 1 Round each addend to the nearest hundred. 477 rounds to 500. 592 rounds to 600. Step 2 Add the rounded numbers. 500 + 600 = 1,100 Step 3 You can get a closer estimate by rounding to a lesser place value. Rounding to the nearest ten gives you addends of 480 and 590. You can add these numbers t ...
... Step 1 Round each addend to the nearest hundred. 477 rounds to 500. 592 rounds to 600. Step 2 Add the rounded numbers. 500 + 600 = 1,100 Step 3 You can get a closer estimate by rounding to a lesser place value. Rounding to the nearest ten gives you addends of 480 and 590. You can add these numbers t ...
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.