'1 error' presentation slideshows

Psych 5510/6510

Psych 5510/6510. Chapter Nine: Outliers and Data Having Undue Influence. Spring, 2009. Effect of Outliers. Set 1: 1, 3, 5, 9, 14 : Sample Mean = est. ? = 6.4, MSE = S 2 = 26.8 Confidence interval mean: 0 ? ? ? 12.8 Set 2: 1, 3, 5, 9, 140 :

613 views • 47 slides

Ensembles

Ensembles. An ensemble is a set of classifiers whose combined results give the final decision. test feature vector. classifier 1. classifier 2. classifier 3. super classifier. result. MODEL * ENSEMBLES. Basic Idea Instead of learning one model Learn several and combine them

461 views • 44 slides

Retroviruses (chapter 38)

Retroviruses (chapter 38). Positive strand RNA viruses (5000-12000 bp) Enveloped Contain reverse transcriptase enzyme Copies RNA into DNA Cause chronic disease long after infection Due to integration of viral DNA into host chromosome. Number of individuals infected with HIV-1.

838 views • 43 slides

Retroviruses (chapter 38)

Retroviruses (chapter 38). Positive strand RNA viruses (5000-12000 bp) Enveloped Contain reverse transcriptase enzyme Copies RNA into DNA Cause chronic disease long after infection Due to integration of viral DNA into host chromosome. Number of individuals infected with HIV-1.

571 views • 43 slides

Center For Software Development Core Java Lecture 2-3 [07/09/211]

Center For Software Development Core Java Lecture 2-3 [07/09/211]. By : Pankaj Vyas. What We have Covered. What is an Object? What is Class ? How to Compile and Execute Your First Java Program? Displaying o/p on System.out Using System.out.println() & System.out.print()

408 views • 27 slides

Evaluating Hypothesis

Evaluating Hypothesis. Introduction Estimating Accuracy Sampling Theory and Confidence Intervals Differences in Error Comparing Learning Algorithms. Introduction. Given some training data a learning algorithm produces a hypothesis.

453 views • 25 slides

Evaluating Hypothesis

Evaluating Hypothesis. Introduction Estimating Accuracy Sampling Theory and Confidence Intervals Differences in Error Comparing Learning Algorithms. Introduction. Given some training data a learning algorithm produces a hypothesis.

309 views • 25 slides

Work

370 views • 24 slides

What is needed to gain the complete control?

291 views • 22 slides

Implementation

222 views • 22 slides

Evaluating Hypothesis

Evaluating Hypothesis. ????????? ? ? ?. ??. Evaluating the accuracy of hypotheses is fundamental to ML. - to decide whether to use this hypothesis - integral component of many learning system Difficulty from limited set of data

211 views • 20 slides

Damage Scoring Flowchart

277 views • 15 slides

CSI5388 Current Approaches to Evaluation

237 views • 13 slides

Machine Learning: Lecture 5

Machine Learning: Lecture 5. Experimental Evaluation of Learning Algorithms (Based on Chapter 5 of Mitchell T.., Machine Learning, 1997). Motivation. Evaluating the performance of learning systems is important because:

162 views • 13 slides

Review Session II

124 views • 12 slides

Error Correction

Error Correction. On Trunk Links. Why bother with error correction?. Forward Error Correction (FEC) allows the receiver to detect and correct errors. It can dramatically reduce the retry rate. For example;. In a transmission with Maxframe 7 and paclen 255, there are 255 x 7 x 8 = 14280 bits.

350 views • 9 slides

Evaluating Classifiers

96 views • 9 slides

CHAPTER 21 numerical integration formulas

79 views • 7 slides

242 views • 5 slides

Nuclear Charge Radius of 6 He - Madhusudan Ojha( PHYSICS 621)

363 views • 4 slides