Web Markov Skeleton Processes and their Applications

Oct 30, 2012

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Web Markov Skeleton Processes and their Applications

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Markov Processes

Markov Processes. Stochastic process movement through a series of well-defined states in a way that involves some element of randomness for our purposes,“states” are microstates in the governing ensemble Markov process stochastic process that has no memory

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Combinatorial Markov Random Fields and their Applications to Information Organization

Research overview. Supervised learning. Unsupervised learning. Web mining. Information retrieval. Clustering. One-class clustering. Multi-modal clustering. Semi-supervised clustering. Focused crawl. Link analysis. Person name disambiguation. Formal model. Feature clustering for text classification.

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Markov Processes

Markov Processes. Aim Higher. What Are They Used For?. Markov Processes are used to make predictions and decisions where results are partly random but may also be influenced by known factors Applications include weather forecasting, economic forecasting, manufacturing and robotics.

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Markov Decision Processes

Markov Decision Processes. Alan Fern *. * Based in part on slides by Craig Boutilier and Daniel Weld. Classical Planning Assumptions. Actions. Percepts. World. sole source of change. perfect. ???? . deterministic. fully observable. instantaneous.

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Testing Processes of Web Applications

Testing Processes of Web Applications. Susan Cohen. Agenda . Brief introduction Web Application Testing Aspects of Web application testing Architectural styles Levels of testing An approach to integration testing Capture/replay Implicit/explicit states Test case generation.

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Markov Decision Processes

Markov Decision Processes. Definitions; Stationary policies; Value improvement algorithm, Policy improvement algorithm, and linear programming for discounted cost and average cost criteria. Markov Decision Process.

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Markov models and applications

Markov models and applications. Sushmita Roy BMI/CS 576 www.biostat.wisc.edu/bmi576/ sroy@biostat.wisc.edu Oct 15 th , 2013. Key concepts. Markov chains Computing the probability of a sequence Estimating parameters of a Markov model Hidden Markov models States

708 views • 29 slides

Markov Decision Processes

Markov Decision Processes. * Based in part on slides by Alan Fern, Craig Boutilier and Daniel Weld. Atomic Model for stochastic environments with generalized rewards. Deterministic worlds + goals of attainment. Stochastic worlds +generalized rewards.

556 views • 38 slides

Markov Decision Processes

Markov Decision Processes. Stochastic, sequential environments. Game show. A series of questions with increasing level of difficulty and increasing payoff Decision: at each step, take your earnings and quit, or go for the next question If you answer wrong, you lose everything. $100 question.

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Markov Decision Processes

Markov Decision Processes. Stochastic, sequential environments (Chapter 17). Image credit: P. Abbeel and D. Klein. Markov Decision Processes. Components: States s , beginning with initial state s 0 Actions a Each state s has actions A(s) available from it

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Markov Decision Processes

Markov Decision Processes. CSE 473 May 28, 2004 AI textbook : Sections 17.2-17.4 - Russel and Norvig Decision-Theoretic Planning: Structural Assumptions and Computational Leverage – Boutilier, Dean, Hanks. Planning. Static vs. Dynamic. Instantaneous vs. Durative. Deterministic

448 views • 30 slides

Markov Decision Processes

Markov Decision Processes. CSE 473 Chapter 17. Problem Classes. Deterministic vs. stochastic actions Full vs. partial observability. Deterministic, fully observable. Stochastic, Fully Observable. Stochastic, Partially Observable. Sequential Decision Problem.

478 views • 28 slides

Markov Decision Processes

Markov Decision Processes. * Based in part on slides by Alan Fern, Craig Boutilier and Daniel Weld. Classical Planning Assumptions. Actions. Percepts. World. sole source of change. perfect. ????. deterministic. fully observable. instantaneous.

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Markov Processes

Markov Processes. MBAP 6100 & EMEN 5600 Survey of Operations Research Professor Stephen Lawrence Leeds School of Business University of Colorado Boulder, CO 80309-0419. Intro to OR Linear Programming Solving LP’s LP Sensitivity/Duality Transport Problems Network Analysis

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Vulnerabilities in web applications and their prevention. Serhii Hamotskyi, NTUU “KPI” shamotskyi@gmail.com @pchr8. Why it’s important. • Protecting a website well always costs less than dealing with a breach or data leak.

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Markov Processes

MGMT E-5070. Markov Processes. Homework Solution. Manual Computer-Based. Machine Operation Problem. A manufacturing firm has developed a transition matrix containing the probabilities that a particular machine will operate or break down

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Markov Decision Processes

Math 419/519 Prof. Andrew Ross. Markov Decision Processes. Highway Pavement Maintenance. Thanks to Pablo Durango-Cohen for this example. Though I have made up the numbers. Classify highway pavement condition as: Good Fair Poor Can do 4 kinds of repairs: Expensive Moderate Cheap

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Markov Processes and Birth-Death Processes

Markov Processes and Birth-Death Processes. J. M. Akinpelu. Exponential Distribution. Definition. A continuous random variable X has an exponential distribution with parameter  > 0 if its probability density function is given by Its distribution function is given by.

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Markov Models and Applications

Markov Models and Applications. Henrik Schiøler, Hans-Peter Schwefel. Mm1 Discrete time Markov processes Mm2 Continuous time Markov processes Mm3 M/M/1 type models Mm4 Advanced queueing models Mm5 Hidden Markov Models and their application (hps).

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Web Usage Mining: Processes and Applications

Web Usage Mining: Processes and Applications. Qiaoyuan Jiang CSE 8331 November 24, 2003. Outline. Brief overview of Web mining Web usage mining Application areas of Web usage mining Future research directions Conclusions. Web Mining.

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