The document discusses using techniques from artificial intelligence such as machine learning, inference, linguistics, and fuzzy logic to develop personalized services that create user profiles and prototypes based on a user's interests and needs to provide tailored information through various channels like web pages, computer interfaces, news, advertising, messages, and data mining. It also describes using Bayesian networks, decision trees, conceptual graphs, and other methods to construct theories of prototypes and information fusion to power these personalized services.
A great power point presentation for DBMS Concepts from start to end and with best examples chapter by chapter. Please go though each chapters sequentially for your knowledge.
A very easy going study material for better understanding and concepts of Database Management System.
A great power point presentation for DBMS Concepts from start to end and with best examples chapter by chapter. Please go though each chapters sequentially for your knowledge.
A very easy going study material for better understanding and concepts of Database Management System.
Leveraging Big Data to Manage Transport Operations (LeMO) project will address these issues by investigating the implications of the utilisation of such big data to enhance the economic sustainability and competitiveness of European transport sector.
Big Data and Harvesting Data from Social MediaR A Akerkar
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Can we see additional value in linking and exploiting big data for business and societal benefit?
If we bring together numerous data sources to provide a single reference point then we start to derive new value.
Until then, we simply risk creating new data silos.
Whilst big data may represent a step forward in business intelligence and analytics, we see added value in linking and utilizing big data for business benefit. Once we bring together numerous data sources to provide a single reference point can we start to derive new value. Until then, we only risk creating new data silos.
Leveraging Big Data to Manage Transport Operations (LeMO) project will address these issues by investigating the implications of the utilisation of such big data to enhance the economic sustainability and competitiveness of European transport sector.
Big Data and Harvesting Data from Social MediaR A Akerkar
The value of social media data is only as valuable as the information and insights we can extract from it. It is the information and insights that will help us make better decisions and give us a competitive edge.
Can You Really Make Best Use of Big Data?R A Akerkar
How big is big? What are the precise criteria for a data set to be considered big data? At least three major factors that contribute to the bigness of big data: Ubiquity and variety of data capturing devices for different types of information
Increase data resolution. Super-linear scaling of data production rate with data producers. Although big data has other dimensions too but these are not inherent to the "bigness" of big data.
Can we see additional value in linking and exploiting big data for business and societal benefit?
If we bring together numerous data sources to provide a single reference point then we start to derive new value.
Until then, we simply risk creating new data silos.
Whilst big data may represent a step forward in business intelligence and analytics, we see added value in linking and utilizing big data for business benefit. Once we bring together numerous data sources to provide a single reference point can we start to derive new value. Until then, we only risk creating new data silos.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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2. Personalisation - user p
profile and p yp
prototypes
to provide: What is received is what is required
EXAMPLES
Web page retrieval to satisfy customer
Computer Interface
News Services Advertising
Services,
Regulation of messages -SMS. E-Mail
Personalised Data Mining
AI Machine Learning, Inference
and Linguistics provides the
intelligent agents for this
personalisation service
R. Akerkar 2
3. message
P1 P2 P3 P4 P5 prototypes
s1 s2 s3 s4 s5 supports for interest
Personalised Fusion
Final Support s
send ? to user
R. Akerkar 3
4. y y
Fuzzy Bayesian Nets
Fuzzy Decision Trees
Can we construct a theory
Fril programs of prototypes ?
Evidential Logic Rules
Can be have a theory of
Neural nets fusion ?
Fuzzy Conceptual graphs
Cluster over persons to get clusters for prototypes
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5. Data Base
WEB
answer fuzzy decision
tree
user query search agent
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6. Fril Evidential logic rule
x1 is g1 with weight w1 through
x2 is g w we g w
s g2 with weight w2 filt
class is f IFF
l i filter
. . . . h
xn is gn with weight wn
For input {xi = fi} f, gi, fi, h are fuzzy sets
Let i = Pr(gi | fi)
(g )
class is f with probability
where
= h (w11 + w22 + … + wnn)
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7. Type company Random Variables
Offer LoanRate size company with conditional
Rate
age independencies
Rate Medical
Offer Comp
Su t
Suit Req
Suit Suit
S it
Company Size :
Loan Interest {large, medium, small}
{l di ll}
Time
Offer : {mortgage, personal loan, car loan, credit card
car insurance, holiday insurance, payment protection,
charge card, home insurance}
R. Akerkar 7
8. Offer : {mortgage, personal loan, car loan, credit card
{mortgage loan loan
car insurance, holiday insurance, payment protection,
charge card, home insurance}
Type Rate : {fixed, variable}
LoanRate : {good, fair, bad}
Company Size : { g , medium, small}
p y {large, , }
Company Age : {old, middle, young}
Loan Period : {long, medium, short}
Rate Suitable : {very, average, little}
{very average
Company Suitable : {very, average, little}
Medical req : {yes, no}
Offer Suit : {good, av, bad}
Interest : {high, medium, low}
R. Akerkar 8
9. X1 X2 X3
We update this prior
X5 joint distribution with
X4
respect to any given
tt i
variable instantiations
X6
n
P (X1 , ..., Xn ) P (X i | P
Pr(X Pr(X Parents(Xi ))
t (X Prior
i
i1
R. Akerkar 9
10. C0 = {Offer*, LoanPeriod*, OfferSuit*}
C0 C1 = {OfferSuit,Offer, RateSuit}
C2 = {RateSuit*, LoanRate*, Offer, TypeRate*}
C3 = {CompSuit, MedReq,* OfferSuit, RateSuit
{CompSuit MedReq * OfferSuit RateSuit,
Interest*}
C1 C4 = {CompSize*, AgeComp*, CompSuit*}
C2 C3
C4
Compile Clique tree : equivalent to forming p
p q q g prior j
joint
distribution using an efficient message passing algorithm
R. Akerkar 10
11. Message :
4% fixed rate long term mortgages available
from 40 year old fairly large Company FRIL
conceptual graph
graph instantiations
Offer = mortgage, Type rate = fixed, loan time = long
CompSize = dist, CompAge = dist.
Use message passing algorithm again to update clique distributions
with graph instantiations.
Result will be distribution over interest variable - defuzzify to
obtain degree of interest
R. Akerkar 11
12. w1 w2 w3 w4 w5 w6 w7 X replaced with {wi}
1
We can also
fuzzify discrete
0 sets
X
Mass Assignment Theory provides:
{Pr(wi | x)} where x is point value, interval or fuzzy set
value
i.e we translate value on X to distribution over {wi}
Mass Assignment Theory provides:
Defuzzification: if {i} is distribution over {wi} then
x i i where i is expected value of wi
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i
13. medium
small large
0 1 2 3 4 5 6
DICE SCORE
Random Set of Voters x ≤3 4 5 6
% accept x as large % 0 20 80 100
large = 4 / 0.2 + 5 / 0.8 + 6 / 1
1 2 3 4 5 6 7 8 9 10
{6} : 0.2,
6 6 6 6 6 6 6 6 6 6 MAlarge ={5, 6} : 0.6,
5 5 5 5 5 5 5 5 {4, 5, 6} : 0.2
4 4
constant threshold : if voter accepts x and y>x then he must accept y
R. Akerkar 13
14. weighted dice is small where small = 1 / 1 + 2 / 0.7 + 3 / 0.3
07 03
prior for dice : 1:0.1, 2:0.1, 3:0.5, 4 :0.1, 5:0.1, 6:0.1
MA = {1} : 0.3, {1, 2} : 0.4, {1, 2, 3} : 0.3
small
Pr(dice is x | small) = probability that a randomly chosen voter
chooses x as value of dice after being told dice value is small
h l f di f b i ld di l i ll
Least Prejudiced Probability Distribution for dice value =
1 : 0 3 + 0.2 + 0.3(1/7) = 0.5429
0.3 0 2 0 3(1/7) 0 5429
2 : 0.2 + 0.3(1/7) = 0.2429 Pr(x | small)
3 : 0.3(5/7) = 0.2143
For continuous fuzzy set we can derive a least prejudiced density
function whose expected value can be used for defuzzification
R. Akerkar 14
15. What is Pr(dice is about_2 | dice is small) where
( )
about 2 = 1 / 0.5 + 2 / 1 + 3 / 0.5
small
0.3 0.4
04 0.3
03
{1} {1, 2} {1, 2, 3}
about_2 Prior
1/2(0.2) 1/7(0.15) 1 : 0.1
0
0 5 {2}
0.5 =01
0.1 = 0.0214
0 0214
2 : 0.1
3 : 0.5
0.15 0.2 0.15
0.5 { , ,
{1, 2, 3} 4 : 0.1
5 : 0.1
Pr(about_2 | small) = 0.6214 6 : 0.1
Point Semantic Unification used to determine Pr(f | g)
where f is fuzzy set, g is point, interval or fuzzy set
R. Akerkar 15
16. prediction
output_sm output_me output_la Means of
output_sm,
output_me,
output me
output_la
are
mutually exclusive fuzzy sets on OUTPUT
t ll l i f t 1, 2, 3
input output_sm : 1
output {distribution over words} output me : 2
output_me
model
output_la : 3
Prediction = 11 + 22 + 33 Defuzzification
R. Akerkar 16
17. Variable X - value x
V i bl l
x is point or fuzzy set defined on R
Bayes Node
Prior { }
{fi} Instantiate : Updated
Distribution {fi : P (fi | x)}
Pr(fi )} Distribution
NET
Classical probability theory does not allow
for this distribution update.
update
R. Akerkar 17
18. P’
Updated
Distribution
o
on
Distribution Constraint X = (X1…Xn)
on Xk - D Minimum Relative Entropy
P
MIN
( )
Pr’(X)
Prior Pr’(X)
Distribution P'(X) Ln Pr(X)
on D satisfied
X = (X1…Xn)
var 1 distribution var 2 distribution
Prior Update Update
until convergence
R. Akerkar 18
19. Bayes Net Compiled to give prior probability distributions.
Update Compiled Net with
input variables instantiated
to probability distributions using
relative entropy updating
Bayes Net message passing algorithms for
Clique tree updating modified to
be equivalent to this relative entropy updating
R. Akerkar 19
20. Fuzzify :
A B C A : same as A above
B : {small, medium, large}
A : {a1, a2, a3} C : {low, av, high}
B : [1, 10]
[1 D : same as above
C : {1, 2, 3, 4, 5, 6}
D D : {d1, d2, d3} small medium large
A B C D
a1 x y {d1, d2} 1 10
= Pr(medium | x) = Pr(large | x)
low = 1 / 1 + 2 / 0.8 + 3 / 0.4
= Pr(low | y) = Pr(av | y)
av = 2 / 0.2 + 3 / 0.6 + 4 / 1 + 5 / 0.2
Pr(d1) = 0.5 Pr(d2) = 0.5
high = 5 / 0.8 + 6 / 1
08
Pr(small | x) = Pr(high | y) = 0
Note
x and y can be point values, intervals or fuzzy sets
R. Akerkar 20
21. Reduced Data Base
Red ced Joint
Probability
A B C D Distribution
a1 medium low d1 .5
5
a1 medium low d2 .5
a1 large low d1 .5
a1 large low d2 .5
a1 medium av d1 .5
a1 medium av d2 .5
a1 large av d1 .5
a1 large av d2 .5
Repeat for all lines of database
Calculate Pr(D | A, B, C)
R. Akerkar 21
22. 1. Search and Scoring based algorithms
1
2. Dependency Analysis algorithms Complexity
(a) n2
A. Node Ordering (b) n4
B.
B Without node ordering
Querying
Markov Cover :
Parents of query node + children of query
node + parents of these children
R. Akerkar 22
23. ((prototype p1 (offer Mortgage) ) (evlog disjunctive (
(AgeOfCompany old_age) 0.1
(CompanySize large_size) 0.15
(LoanRate small rate) 0.4
small_rate)
(TypeRate quite_fixed) 0.1
(LoanPeriod long_period) 0.2
(MedicalRequired bias_to_yes) 0.05))) : ((1 1) (0 0))
((prototype p1 (offer PersonalLoan) ) (evlog disjunctive ( Rules can also
(AgeOfCompany old_age) 0.1 be used for
(
(CompanySize large_size) 0.15
p y g ) fusion
(LoanRate medium_rate) 0.4
(TypeRate quite_variable) 0.1
(LoanPeriod short_period) 0.2
(MedicalRequired bi t
(M di lR i d bias_to_no) 0.05))) : ((1 1) (0 0))
) 0 05)))
ETC - rules for other offers and other prototypes
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