問題一覧
1
data process large number ?
ann
2
receives signals
dendrites
3
sum signals
soma
4
simple process elemnt
neurond
5
net consists larg number neurons
neural
6
neuron connected to other neurons
associated weight
7
neuron has internal state
activation
8
rrpresent information used net to solve problem
weight
9
set connected input ,output
neural network
10
remove links
pruning
11
linear&non linear
svm
12
learn from example
supervised learning
13
data labded with defin class
supervition
14
random error
noisy data
15
close to mean ,large range
standard deviation
16
line within box
median
17
to line minimum,maximum
whidkers
18
point beyound outlier
outliers
19
represent with box ,hight box IQR
box plot
20
set computational predures
gentic algorthim
21
new generation
reproduction
22
random position
crossover
23
change in situation
mutation
24
difficult apply people member ship
fuzzy logic
25
technology blended utilize
integrated system
26
cimbin fuzzey logic with neural netwotks
fuzzy neural network
27
data target attribute
supervised learning
28
data no target atribute
unsupervised learning
29
set cluster
clustering
30
data object non overlapping
partitonal clustering
31
cluster orginized
hirarchical clustering
32
cluster closer to every point
well separated
33
cluster closer to center ,or cluster is centroid
center based
34
cluster closer to one or more
contiguity based
35
share some common property
conceptual cluster
36
region point ,separated by low density
density based
37
graph frequence
histogram
38
combine data from muliple sources
data integration
39
plot useful for providing first lock
correlation relationships
40
remove noise from data
smoothing
41
summarization data cube constraction
aggregation
42
hierarchy climbing
generlization
43
scaled fall within small
normalization
44
new atrribute
attribute feature constraction
45
average largest smallest
midrange
46
reduce representation data set result
data reduction
47
distance between first,third
IQR
48
data set made data object
data object
49
represent entity
data object
50
described by attribute
data object
51
observed value for attribute
observation
52
set attribute
attribute vector
53
data field representing characteristics or feature
attribute va fe di
54
quantitative,quntity
numeric attribute
55
regularintervals data distributin
quantiles
56
sighnificance important
weigh arthimetic mean
57
shoppinh high ,low
trimmed mean shopping value
58
percentage test set
accuray rate