Sub-Markov Random Walk for Image
... is absorbed at current node i with a probability αi and follows a random edge out of it with probability 1 − αi . And they analyze the relations between PARW and other popular ranking and classification models, such as PageRank [7], hitting and commute times [32], and semisupervised learning [11], ...
... is absorbed at current node i with a probability αi and follows a random edge out of it with probability 1 − αi . And they analyze the relations between PARW and other popular ranking and classification models, such as PageRank [7], hitting and commute times [32], and semisupervised learning [11], ...
Hidden Markov Models
... If P( X|fair coin) > P(X|biased coin), then the dealer most likely used a fair coin. If P( X|fair coin) < P(X|biased coin), then the dealer most likely used a biased coin. The probabilities of getting fair coin and biased coin will be equal at k = n/ log23. If k < n/ log23 dealer uses fair co ...
... If P( X|fair coin) > P(X|biased coin), then the dealer most likely used a fair coin. If P( X|fair coin) < P(X|biased coin), then the dealer most likely used a biased coin. The probabilities of getting fair coin and biased coin will be equal at k = n/ log23. If k < n/ log23 dealer uses fair co ...
ch11.5-13
... bulbs contains six yellow, six white and 12 purple crocus bulbs. One of the two packages is selected at random. a) If three bulbs from this package were planted and all three yielded purple flowers, compute the conditional probability that the package B was selected. (Answer: P(B|PPP) = 55/69) b) If ...
... bulbs contains six yellow, six white and 12 purple crocus bulbs. One of the two packages is selected at random. a) If three bulbs from this package were planted and all three yielded purple flowers, compute the conditional probability that the package B was selected. (Answer: P(B|PPP) = 55/69) b) If ...
Seminar Slides - CSE, IIT Bombay
... and never missed any city, where as SOM is capable of missing cities. Concurrent Neural Network is very erratic in behavior , whereas SOM has higher reliability to detect every link in smallest path. Overall Concurrent Neural Network performed poorly as compared to SOM. ...
... and never missed any city, where as SOM is capable of missing cities. Concurrent Neural Network is very erratic in behavior , whereas SOM has higher reliability to detect every link in smallest path. Overall Concurrent Neural Network performed poorly as compared to SOM. ...
Hidden Markov Models
... NB Observations are mutually independent, given the hidden states. (Joint distribution of independent variables factorises into marginal distributions of the ...
... NB Observations are mutually independent, given the hidden states. (Joint distribution of independent variables factorises into marginal distributions of the ...
CURRICULUM PLAN
... 2. Elimination method (solutions are integers or easy rational solutions ( ½ , ¼ , 1/3 etc)). 3. Substitution method (solutions are integers or easy rational solutions ( ½ , ¼ , 1/3 etc)). 4. Use simultaneous solutions for solving life like problems. Quadratic Functions Students should be able to: ...
... 2. Elimination method (solutions are integers or easy rational solutions ( ½ , ¼ , 1/3 etc)). 3. Substitution method (solutions are integers or easy rational solutions ( ½ , ¼ , 1/3 etc)). 4. Use simultaneous solutions for solving life like problems. Quadratic Functions Students should be able to: ...
Applications of Number Theory in Computer Science Curriculum
... A total of 102 students from both institutions participated in the pre and post survey ...
... A total of 102 students from both institutions participated in the pre and post survey ...
Simulated annealing
Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of locating a good approximation to the global optimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more efficient than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution.The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. Both are attributes of the material that depend on its thermodynamic free energy. Heating and cooling the material affects both the temperature and the thermodynamic free energy. While the same amount of cooling brings the same amount of decrease in temperature it will bring a bigger or smaller decrease in the thermodynamic free energy depending on the rate that it occurs, with a slower rate producing a bigger decrease.This notion of slow cooling is implemented in the Simulated Annealing algorithm as a slow decrease in the probability of accepting worse solutions as it explores the solution space. Accepting worse solutions is a fundamental property of metaheuristics because it allows for a more extensive search for the optimal solution.The method was independently described by Scott Kirkpatrick, C. Daniel Gelatt and Mario P. Vecchi in 1983, and by Vlado Černý in 1985. The method is an adaptation of the Metropolis–Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by M.N. Rosenbluth and published in a paper by N. Metropolis et al. in 1953.