GENERAL-
IZATION
8 CHAPTER 1. INTRODUCTION
Width
22 salmon . sea bass
.
Lightness
2 4 6 8 1 0
Figure 1.5: Overly complex models for the sh will lead to decision boundaries that are
complicated. Whil
Strain improvement
Strain Improvement
What is the Need?
With the exception of the food industry, only a
few commercial fermentation processes use
wild strains isolated directly from nature.
Mutated
SOMATIC HYBRIDIZATION
Somatic hybridization technique
1. isolation of protoplast
2. Fusion of the protoplasts of desired species/varieties
3. Identification and Selection of somatic hybrid cells
4. Cu
SRM UNIVERSITY
DEPARTMENT OF BIOTECHNOLOGY
SCHOOL OF BIOENGINEERING
LESSON PLAN FOR BT204 BIOPROCESS PRINCIPLES
UNIT NO
TOPIC
HOURS
GIVEN
Historical development of bioprocess technologies,
Role of bio
Kla Volumetric mass transfer coefficient
Mass transfer between a moving liquid and solid is important in biological processing in a
variety of applications.
Gas-liquid mass transfer is of paramount im
MONOD MODEL
(UNSTRUCRURED MODEL ON SEGREGATED MODEL)
Assumptions
This model assumes that a single chemical species, S, is
growth limiting.
The Monod equation is semi-empirical ; it derives from the
p
UNIT-3
Metabolic Stoichiometric Energetics
Yield coefficients based on other substrates or product formation may be
defined as:
Yield coefficient Yx/s= -X/S
Apparent Yield coefficient Yx/o2 = -X/O2
Ob
UNIT -1 : INTRODUCTION TO BIOPROCESS
Historical development of bioprocess technologies,
Role of bioprocess engineer in the biotechnology
industry,
Concept of Bioprocess,
Outline of an integrated bi
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DECISION
RULE
4 CHAPTER 2. BAYESIAN DECISION THEORY
assume that any incorrect classication entails the same cost or consequence, and that
the only information we are allowed to use is the value of the
18 CHAPTER 1. INTRODUCTION
distributions are unknown and even the category membership of training patterns is
unknown. We begin in Chap. ? (Bayes decision theory) by considering the ideal case
in whic
RECEIVER
OPERATING
CHARACTER-
ISTIC
34 CHAPTER 2. BAYESIAN DECISION THEORY
d' = M (76)
a
A high d is of course desirable.
While we do not know 1, #2, (I nor a:*, we assume here that we know the state
2.11. PROBLEMS 45
4. Generalize the minimax decision rule in order to classify patterns from three
categories having triangle densities as follows:
6- w- 6,? for :6- -<6-
Marlow) = T(Mi,6) E cfw_ 81 M
44 CHAPTER 2. BAYESIAN DECISION THEORY
Problems
69 Section 2.1
1. In the two-category case, under the Bayes decision rule the conditional error
is given by Eq. 7. Even if the posterior densities are c
2.10. *MISSING AND N OISY FEATURES 39
The surface g(x) = 0 from Eq. 88 is shown on the left of the gure. Indeed, as we
might have expected, the boundary places points with two or more yes answers into
48 CHAPTER 2. BAYESIAN DECISION THEORY
(b) Plot these discriminant functions and the decision regions for the two-category
one-dimensional case having
0 p(x|w1) ~ N(1, 1),
0 P(-I3lw2l ~ N(1,1),
o P(w1
UNIT IV
INTERFACING
DEVICES
SYLLABUS
8255 programmable peripheral interface
8257/8237 programmable DMA controller
8279 keyboard/display interfacing
8253/8254 Programmable interval timer
Need of Inter
Chapter 2
AUTOMATIC LOAD FREQUENCY
CONTROL
1. INTRODUCTION
This chapter deals with the control mechanism needed to maintain the system
frequency. The topic of maintaining the system frequency constant
Linear Discriminant Functions
Chapter 5 (Duda et al.)
CS479/679 Pattern Recognition
Dr. George Bebis
Discriminant Functions:
two-categories case
Decide 1 if g(x) > 0 and 2 if g(x) < 0
If g(x)=0, the
Chapter 1: Introduction to Pattern
Recognition
Machine Perception
An example
Pattern Recognition Systems
The Design Cycle
Learning and Adaptation
Conclusion
All materials used in this course were
A Non-Parametric Bayesian Method for
Inferring Hidden Causes
Outline
Introduction
A generative model with hidden causes
Inference algorithms
Experimental results
Conclusions
Introduction
A variety of
Automated Drug Delivery System
Drug deliveryrefers to approaches, formulatons,
technologies, and systems for transportng a
pharmaceutcalcompound in the body as needed to
safely achieve its desiredther
Conceptual Dependency
Lecture Module 17
Conceptual Dependency (CD)
CD theory was developed by Schank in 1973 to 1975
to represent the meaning of NL sentences.
It helps in drawing inferences
It is i
CS 2108-Pattern Recognition
DR.Prabakaran S
10/20/16
1
Pattern.?
A pattern is a set of objects or phenomena
or concepts where the elements of the set are
similar to one another in certain ways or
aspe
L4: Bayesian Decision Theory
Likelihood ratio test
Probability of error
Bayes risk
Bayes, MAP and ML criteria
Multi-class problems
Discriminant functions
CSCE 666 Pattern Analysis | Ricardo Gutierrez-
Introduction to Pattern
Recognition
Machine Perception
An example
Pattern Recognition Systems
The Design Cycle
Learning and Adaptation
Conclusion
Machine Perception
Build a machine that can rec