14
Combining
Models
In earlier chapters, we have explored a range of different models for solving classication and regression problems. It is often found that improved performance can be
obtained by combining multiple models together in some way, instead

2
Probability
Distributions
In Chapter 1, we emphasized the central role played by probability theory in the
solution of pattern recognition problems. We turn now to an exploration of some
particular examples of probability distributions and their propert

4
Linear
Models for
Classication
In the previous chapter, we explored a class of regression models having particularly
simple analytical and computational properties. We now discuss an analogous class
of models for solving classication problems. The goal

Wireshark Lab: NAT v6.0
Supplement to Computer Networking: A Top-Down
Approach, 6th ed., J.F. Kurose and K.W. Ross
Tell me and I forget. Show me and I remember. Involve me and I
understand. Chinese proverb
2005-21012, J.F Kurose and K.W. Ross, All Rights

13
Sequential
Data
So far in this book, we have focussed primarily on sets of data points that were assumed to be independent and identically distributed (i.i.d.). This assumption allowed
us to express the likelihood function as the product over all data

7
Sparse Kernel
Machines
In the previous chapter, we explored a variety of learning algorithms based on nonlinear kernels. One of the signicant limitations of many such algorithms is that
the kernel function k(xn , xm ) must be evaluated for all possible

5
Neural
Networks
In Chapters 3 and 4 we considered models for regression and classication that comprised linear combinations of xed basis functions. We saw that such models have
useful analytical and computational properties but that their practical appl

9
Mixture Models
and EM
Section 9.1
If we dene a joint distribution over observed and latent variables, the corresponding distribution of the observed variables alone is obtained by marginalization. This
allows relatively complex marginal distributions ov

11
Sampling
Methods
For most probabilistic models of practical interest, exact inference is intractable, and
so we have to resort to some form of approximation. In Chapter 10, we discussed
inference algorithms based on deterministic approximations, which

3
Linear
Models for
Regression
The focus so far in this book has been on unsupervised learning, including topics
such as density estimation and data clustering. We turn now to a discussion of supervised learning, starting with regression. The goal of regr

Pattern Recognition and Machine Learning
Errata and Additional Comments
Markus Svens n and Christopher M. Bishop
e
All rights retained. Not to be distributed without permission.
c Markus Svens n and Christopher M. Bishop (2007)
e
April 29, 2008
2
Preface

6
Kernel
Methods
Chapter 5
Section 2.5.1
In Chapters 3 and 4, we considered linear parametric models for regression and
classication in which the form of the mapping y(x, w) from input x to output y
is governed by a vector w of adaptive parameters. During

1
Introduction
The problem of searching for patterns in data is a fundamental one and has a long and
successful history. For instance, the extensive astronomical observations of Tycho
Brahe in the 16th century allowed Johannes Kepler to discover the empir

12
Continuous
Latent
Variables
Appendix A
In Chapter 9, we discussed probabilistic models having discrete latent variables, such
as the mixture of Gaussians. We now explore models in which some, or all, of the
latent variables are continuous. An important

8
Graphical
Models
Probabilities play a central role in modern pattern recognition. We have seen in
Chapter 1 that probability theory can be expressed in terms of two simple equations
corresponding to the sum rule and the product rule. All of the probabil

Information Science and Statistics
Series Editors:
M. Jordan
J. Kleinberg
B. Scholkopf
Information Science and Statistics
Akaike and Kitagawa: The Practice of Time Series Analysis.
Bishop: Pattern Recognition and Machine Learning.
Cowell, Dawid, Lauritzen

Appendix A. Data Sets
In this appendix, we give a brief introduction to the data sets used to illustrate some
of the algorithms described in this book. Detailed information on le formats for
these data sets, as well as the data les themselves, can be obta

10
Approximate
Inference
A central task in the application of probabilistic models is the evaluation of the posterior distribution p(Z|X) of the latent variables Z given the observed (visible) data
variables X, and the evaluation of expectations computed

Wireshark Lab: IP v6.0
Hnh 1. ICMP Echo Request message IP information
1)
a ch IP ca my tnh l: 192.168.1.26
2)
Trong phn header, gi tr ca giao thc vi lp trn l: ICMP (1)
3)
S bytes cha trong IP header l 20 bytes, tng s bytes gi l 56, nh vy
c th suy ra s by

Hidden Markov Models
Lecture Notes
Speech Communication 2, SS 2004
Erhard Rank/Franz Pernkopf
Signal Processing and Speech Communication Laboratory
Graz University of Technology
Inffeldgasse 16c, A-8010 Graz, Austria
Tel.: +43 316 873 4436
E-Mail: rank@in

Solution to Wireshark Lab: Ethernet and ARP
Fig. 1 GET request Ethernet information
1. What is the 48-bit Ethernet address of your computer?
The Ethernet address of my computer is 00:09:5b:61:8e:6d
2. What is the 48-bit destination address in the Ethernet

Wireshark Lab 3 TCP
The following reference answers are based on the trace files provided with the text book,
which can be downloaded from the textbook website.
TCP Basics
Answer the following questions for the TCP segments:
1. (1 point) What is the IP ad

Note to other teachers and users of these slides. Andrew would be delighted if you found
this source material useful in giving your own lectures. Feel free to use these slides
verbatim, or to modify them to fit your own needs. PowerPoint originals are ava

Hidden Markov Models
A Tutorial for the Course Computational Intelligence
http:/www.igi.tugraz.at/lehre/CI
Barbara Resch (modied by Erhard and Car line Rank)
Signal Processing and Speech Communication Laboratory
Ineldgasse 16c/II
phone 8734436
Abstract
Th

Java Code Conventions
September 12, 1997
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