machine learning tutorial part 2

8/3/2019 Machine Learning Tutorial Part 2

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Machine Learning for... OKKAMoids Part IIDelving Deeper: Models, Parameter Estimation, and ML in

Practice

George Giannakopoulos

April 13, 2010

George Giannakopoulos Machine Learning for... OKKAMoids Part I I
http://find/http://goback/


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IntroductionClassifying sequences

Parameter EstimationIn Practice

Closing

Connection to Part IDefinitions

In the previous episode...

Learning from Experience

Various tasks

Differentiation based on input (classification, clustering, ...)

Differentiation based on inductive bias (strategy)

Several ways to evaluate performance



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Closing


Purpose

Part II: Delving Deeper

We will touch the following subjects:

How can I classify sequences?

Aspects of modeling and parameter estimation

Good Practices for Using Machine Learning Techniques

Matching Problems to AlgorithmsAvailable Tools


I d i


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Closing


Pattern Recognition

Pattern Recognition is the scientific discipline whose goal is theclassification of objects into a number of categories orclasses. [Theodoridis and Koutroumbas, 2003]


I t d ti


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Closing


Sequence

A sequence is an ordered list of terms:

S is a set , f :I

S

Strings vs. time-series

abcdacde: A string or symbol sequence

(0.2, 0.4, 0.3, 0.1, 2.1): A time series



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Introduction


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Closing

OverviewTypes of Modeling

Classifying sequences The models

Deterministic Grammars and

Automata [Cicchello and Kremer, 2003]Probabilistic State Machines and BayesianNetworks [Heckerman, 2008]

Constraint Satisfaction (e.g. N-gram

Graphs [Giannakopoulos, 2009])


Introduction


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Closing


Classifying sequences What is this all about?

Observations, i.e. what we see, the output

Optionally (Hidden) States (also termed labels), i.e. whatcauses the observations

Parameters, i.e. the details of the cause that determines theoutput or of the model that explains it


Introduction


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Closing


An example Hidden Markov Model


Introduction


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Classifying sequencesParameter Estimation

In PracticeClosing


Modeling Discriminative vs. Generative Models (1)

Discriminative model: does not take into account previousobservations

Generative model: takes into account previous observations


Introduction


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In PracticeClosing


Modeling Discriminative vs. Generative Models (2)

Discriminative model: No assumption for the observations(e.g. Conditional Random Fields: see [Wallach, 2004] for an

introduction)Generative models: Some assumptions for the observations(e.g. Hidden Markov Models: see [Rabiner, 1989] for aintroduction tutorial)

Which is better? See: [Long and Servedio, 2006],[Jebara and Meila, 2006]1

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See also http://tinyurl.com/y7yat5q for a related discussion.George Giannakopoulos Machine Learning for... OKKAMoids Part I I

Introduction
http://tinyurl.com/y7yat5qhttp://tinyurl.com/y7yat5qhttp://find/http://goback/


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In PracticeClosing

The BasicsModelsModel Optimization

Example of Model with Parameters

SPRINKLERSPRINKLERSPRINKLER RAIN

GRASS WET

T F

SPRINKLER

0.4 0.6

T F

RAIN

0.2 0.8

SPRINKLER F

GRASS WET

0.0 1.0

TRAIN

FF

0.8 0.2TF

0.9 0.1FT

0.99 0.01TT

RAIN

F

0.01 0.99T


IntroductionC f


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In PracticeClosing


The Question

What is the best model to describe a set ofobservations/instances?


IntroductionCl if i Th B i


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In PracticeClosing


Searching for a Model

Assumptions (Independence, Underlying ProbabilityDistributions, etc.)

A priori knowledge (Previous Studies, Expert Knowledge, etc.)


IntroductionCl ssif i s s Th B si s


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In PracticeClosing


Parameteric vs. Non-parametric Approaches

Parametric approaches: Stable set of unknown parameters(e.g. Power-law parameters)

Non-parametric approaches: Determined set of unknownparameters, based on the learning (e.g. histogram)


IntroductionClassifying sequences The Basics


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In PracticeClosing


Refining a Model (1)

Example

For a gaussian distribution find the best parameters (, ) thatdesribe the following values:0.7168090 0.6515225 0.6213850 -0.6626706 -1.1918936 0.7711588-3.1388009 0.2561228 1.1569174 0.6771980




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In PracticeClosing


Refining a Model (2)

Best parametersMean: 0.01422517, St. Dev: 1.311977

Usually, we have to search in the parameter space.




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In PracticeClosing


Searching for Optimal Parameters

Small search space Brute force methods

High-speed approximation Greedy techniques

No local maxima Gradient descent

Small plateau Simulated annealing

Little known Evolutionary (genetic) algorithms



T l


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In PracticeClosing

ToolsFrom Problem to Solution

ML tools

WEKA: Many algorithms

HMM: JaHMM, Hidden Markov Model Toolbox

CRF: CRF for JavaSVM: LibSVM, SVMLite

Time Series: Gnu Regression, Econometrics and Time-seriesLibrary (Gretl), Rapid-I (former YALE)

Constraint-based: JINSECT

2

MLOSS3

2See http://sourceforge.net/projects/jinsect/.3

See http://mloss.org/software/ for many tools.George Giannakopoulos Machine Learning for... OKKAMoids Part I I


Tools
http://sourceforge.net/projects/jinsect/http://sourceforge.net/projects/jinsect/http://mloss.org/software/http://mloss.org/software/http://sourceforge.net/projects/jinsect/http://find/http://goback/


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y g qParameter Estimation

In PracticeClosing


Questions

What data do I have/need?What is the data type: sequence or

What do I want to learn?

What do I know beforehand?



Tools


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Closing


Considerations

What are the features?



Tools


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Closing


Considerations


What do they mean?



P E i iTools


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Closing


Considerations


What do they mean?Is there an obvious connection to the class?



P t E ti tiTools


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Closing

From Problem to Solution

Considerations



Feature vector: what does every dimension represent insimple words?



Parameter EstimationTools


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Closing

From Problem to Solution

Considerations



Feature vector: what does every dimension represent insimple words?

Can I describe in a sentence what my instance is?



Parameter EstimationWhat did we talk about today?


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Closing

Is that all?

Recapitulation

Sequences

Models

Parameters

Tools

Practices



Parameter EstimationWhat did we talk about today?


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Closing

Is that all?

Yes, we are done! If you need more, see the references in

Section 6

Thank you!Please check the feedback form4 to help me improve.

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http://tinyurl.com/ycommj3George Giannakopoulos Machine Learning for... OKKAMoids Part I I

References
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References

(2010).

Wikimedia commons.Cicchello, O. and Kremer, S. C. (2003).Inducing grammars from sparse data sets: a survey ofalgorithms and results.J. Mach. Learn. Res., 4:603632.

Colton, S. (March 30, 2010).Artificial intelligence course v231.

Giannakopoulos, G. (2009).Automatic summarization from multiple documents.PhD thesis, Ph. D. dissertation, Department of Informationand Communication Systems Engineering, University of theAegean, Samos, Greece, http://www. iit. demokritos.gr/ggianna/thesis. pdf.

Heckerman, D. (2008).George Giannakopoulos Machine Learning for... OKKAMoids Part I I

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