Map Generalization

Map Generalization. Introduction Concepts conventional cartography geographic information systems Developments conceptual models algorithms knowledge representation. Image Processing Division. Introduction. Data presentation display communication Data integration

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Glittering Generalization

Glittering Generalization. Weasel words used. Statement jumps from a few cases to all. “Glittering” because it’s falsely attractive Often used by politicians.

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Generalization Specialization

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A. GENERALIZATION

A. GENERALIZATION. Generalization – how to decide?. Scale of the map may dictate your needs May seem arbitrary at times, although it helps to simplify for easier viewing sometimes Also, well-defined points can be important Why? . Generalization: Standards.

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Hasty Generalization

Hasty Generalization. Tyler McCown & Luke Button. What is a Hasty Generalization?. Also known as a Converse Accident Drawing a conclusion based on one or several atypical situations. Made with insufficient evidence Sometimes carelessly selected evidence. The Crucible.

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Model generalization

Model generalization. Test error Bias, variance and complexity In-sample error Cross-validation Bootstrap “Bet on sparsity ”. Model. Training data. Testing data. Model. Testing error rate. Training error rate.

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Generalization

Generalization. TLW: Evaluate and apply generalization to my teaching practice I will: Define generalization and its characteristics through reading and discussion Read and evaluate generalizations, discuss with a partner, and create a plan for application.

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Terrain Generalization : Recommendations

Terrain Generalization : Recommendations. 3 breaks 24K – 250K 250K – 750K 750K – 1M Processing Steps 10m DEM, smoothed once (LGDEM) 10m DEM, smoothed 10 times (MDDEM) 30m DEM aggregated, smoothed 10 times (SMDEM). Grand Canyon Rugged Terrain. 24 50 100 250 500 750 1M.

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Hasty Generalization

Hasty Generalization. A hasty generalization is a broad claim based on too-limited evidence. Tristan Shaw. WWW.FRENCH.ABOUT.COM. http://i.ytimg.com/vi/T64H0Gyw2D0/0.jpg. Example 1. Act #1, page 189, speaker: Mrs. Putnam

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Stereotype vs. Generalization

Stereotype vs. Generalization. What’s the difference?. Definition of Generalization. A generalization is taking one or a few facts and making a broad statement. Generalizations can be false or harmful.

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Classification, Generalization, and Specialization

Classification, Generalization, and Specialization. Classification. Objects with similar definitions have been grouped and defined into classes. The act of identifying and categorizing similar objects into classes is known as classification in object-oriented modeling.

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Fallacy #9 Hasty Generalization

Fallacy #9 Hasty Generalization. is a fallacy committed when a person draws a conclusion about a population based on a sample that is not large enough. Example.

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Generalization and Reasoning example:

Generalization and Reasoning example: . Assume that what is true of one or some members of a class will also be true of others or the whole class. The evidence is the presentation of specific intense. The inference is that these are representative of others. . Cause ( Causal Arguments) :.

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Chapter 7 Generalization/Specialization and Inheritance

Chapter 7 Generalization/Specialization and Inheritance. Chapter 7 Topics. Creating generalization/specialization hierarchies using the extends keyword to implement inheritance with problem domain classes Invoking superclass constructors when writing a subclass constructor

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Chapter 8: Generalization and Function Approximation

Chapter 8: Generalization and Function Approximation. Look at how experience with a limited part of the state set be used to produce good behavior over a much larger part. Overview of function approximation (FA) methods and how they can be adapted to RL. Objectives of this chapter:.

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Generalization Web Services

Generalization Web Services. University of Zurich. Moritz Neun. Motivation. Motivation 1.

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Generalization from empirical studies

Generalization from empirical studies. Tore Dybå: Session introduction (~20 min.) Erik Arisholm: Generalizing results through a series of replicated experiments on software maintainability (~20 min.) Jeff Carver: Methods and tools for supporting generalization (~20 min.)

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Generalization

Generalization. A generalization is a kind of conclusion. Clue Words indicate a generalization. KEY WORDS. NEVER. ALL. NONE. MOST. SOMETIMES. SELDOM. OVERALL. IN GENERAL. EVERYONE. ALWAYS. USUALLY. FEW.

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Types of Generalization

Types of Generalization. Stimulus generalization occurs when behaviors are performed in settings or in the presence of stimuli other than those in which the behaviors were taught.

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What is a generalization ?

What is a generalization ?. A generalization is a broad statement about a group of people, things, or ideas. It states something they have in common. Valid Generalizations. Valid means true. Supported by facts Uses logic and reasoning Proven with several examples. Faulty Generalizations.

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Pattern Associators, Generalization, Processing

Pattern Associators, Generalization, Processing. Psych 85-419/719 Feb 6, 2001. A Pattern Associator. Consists of a set of input units, and output units, and connections from input to output. .. And a training set of examples, consisting of inputs and their corresponding outputs.

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A. GENERALIZATION

A. GENERALIZATION. Generalization – how to decide?. Scale of the map may dictate your needs May seem arbitrary at times, although it helps to simplify for easier viewing sometimes Also, well-defined points can be important Why?. Generalization: Standards.

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