José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Sequence Classification Using.

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Sequence Classification Using Statistical Pattern Recognition José Antonio Iglesias, Agapito Ledezma, and Araceli Sanchis Computer Science Department Universidad Carlos III de Madrid Avda. de la Universidad, 30. 28911 Leganés, Spain {jiglesia, ledezma, masm}@inf.uc3m.es.

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition  Motivation and Introduction  Sequence classification  Our approach  L ibrary Creation  Classification  Target Environment  Description  Experiments and Results  Conclusions and Future Works Outline. 1

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition  Motivation and Introduction  Sequence classification  Our approach  L ibrary Creation  Classification  Target Environment  Description  Experiments and Results  Conclusions and Future Works 1 Outline

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Opponent behavior Modelling / Classification (Environment: soccer simulation domain) Motivation. 2 Opponent Modeling Pattern Recognition Off-Line Analysis No-Pattern LogFile Pattern LogFile Base Estrategy Pattern Recognized Patterns On-Line Comparing Method Pattern Detection On-Line Detection Environment Information Advices to Players RoboCup Soccer Server Pattern Recognized

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Behavior Classification Behavior as sequence of elements Sequence Classification Introduction. 3 Sequence: “set of elements ordered so that they can be labelled with the positive integers” (Merriam-Webster Dictionary)

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Motivation and Introduction Sequence classification  Sequence classification  Our approach  L ibrary Creation  Classification  Target Environment  Description  Experiments & Results  Conclusions and Future Works 4 Outline

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Given: Classes = {c 1, c 2, … c n } Sequence E = {e 1, e 2, … e n } Determine: Which class c i Є C does the sequence E belong to. Sequence classification 5

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Motivation and Introduction Sequence classification Our approach  Our approach  L ibrary Creation  Classification  Target Environment  Description  Experiments & Results  Conclusions and Future Works. 6 Outline

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Our approach pwd fs fg … vi man ls … … finger more ls... Sequence 1 Class 1 Sequence 2 Class 2 Sequence n Class n Pattern 1Pattern 2Pattern 3 … Pattern Library Library CreationClassification vi more ls … Pattern to classify Sequence to classify Compare_ Patterns … On-Line Sequence Classification SEQUENCE CLASS Classification Result. 7

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Motivation and Introduction Sequence classification Our approach  Our approach Library Creation  Library Creation  Classification  Target Environment  Description  Experiments & Results  Conclusions and Future Works 8 Outline

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Library Creation. Trie (retrieval) data structure: Special search tree used for storing elements and its prefixes. Every node: – –represents an element –stores useful information (times appeared,…) 9

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Library Creation - An example trie Sequence to insert initially in the trie: {pwd  vi  pwd  vi  pwd  ls} pwd vi pwd vi pwd ls Sequence 10

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Library Creation - An example trie Sequence to insert initially in the trie: {pwd  vi  pwd  vi  pwd  ls} Sub-sequence length: 3 {pwd  vi  pwd  vi  pwd  ls} Sub-sequences to insert in the trie: {pwd  vi  pwd} and {vi  pwd  ls} pwd vi pwd vi pwd ls Sequence 10

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Library Creation- An example trie Library Creation - An example trie Sub-sequences to insert in the trie: pwd  vi  pwd {pwd  vi  pwd} and {vi  pwd  ls} Root 11

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Library Creation- An example trie Library Creation - An example trie Sub-sequences to insert in the trie: pwd  vi  pwd {pwd  vi  pwd} and {vi  pwd  ls} Root pwd [1]vi [1]pwd [1] 11

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Library Creation- An example trie Library Creation - An example trie Sub-sequences to insert in the trie: vi  pwd {pwd  vi  pwd} and {vi  pwd  ls} Root pwd [1]vi [1]pwd [1]vi [1]pwd [1] 11

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Library Creation- An example trie Library Creation - An example trie Sub-sequences to insert in the trie: vi  pwd {pwd  vi  pwd} and {vi  pwd  ls} Root pwd [2]vi [1]pwd [1]vi [1]pwd [1] 11

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Library Creation - An example trie Sub-sequences to insert in the trie: vi  pwd {pwd  vi  pwd} and {vi  pwd  ls} Root pwd [2]vi [1]pwd [1]vi [2]pwd [2]ls [1] 11

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Library Creation- An example trie Library Creation - An example trie Sub-sequences to insert in the trie: vi  pwd {pwd  vi  pwd} and {vi  pwd  ls} Root pwd [3]vi [1]pwd [1]vi [2]pwd [2]ls [1] 11

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Library Creation - An example trie Sub-sequences to insert in the trie: vi  pwd {pwd  vi  pwd} and {vi  pwd  ls} Root pwd [3]vi [1]pwd [1]vi [2]pwd [2]ls [1] 11

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Library Creation - An example trie vi  pwd  {pwd  vi  pwd  vi  pwd  ls} Root pwd [3]vi [1]pwd [1]vi [2]pwd [2]ls [1] 11 pwd vi pwd vi pwd ls

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Evaluate the relation/dependence between an element and its prefix Two approaches: – Frequency-based method.  Statistical dependence method. Our approach: Statistical Value used: Chi-square value. This value is stored in every node of the trie Library Creation - Evaluating Dependences 12

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Event Different event Total PrefixO 11 O 12 O 11 + O 12 Different Prefix O 21 O 22 O 21 + O 22 TotalO 11 + O 21 O 12 + O 22 O 11 + O 12 + O 21 + O 22 O 11 : How many times the current node/element is followed by its prefix. O 12 : How many times the current node/e lement is followed by a different prefix. O 21 : How many times a different prefix (of the same length) is followed by the same node. O 22 : How many times a different prefix (of the same length) is followed by a different node. Expected (E ij )= (Row i Total x Column j Total) Grand Total X 2 = ∑ ∑ (O ij - E ij ) 2 E ij i=1 r k 2 x 2 Contingency Table Library Creation - Evaluating Dependences j=1. 13

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Library Creation - Evaluating Dependences. pwd [3] [5.1] vi [1] [5.1] [4.3] pwd [1] [4.3]vi [2] [3.5] pwd [2] [3.5] [4.3] ls [1] [4.3] ls [2] Sequence Pattern Trie Root  A Sequence Pattern Trie is created for each class. 14

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Motivation and Introduction Sequence classification Our approach  Our approach Library Creation Classification  Classification  Target Environment  Description  Experiments & Results  Conclusions and Future Works 15 Outline

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Classification pwd fs fg … vi man ls … … finger more ls... Sequence 1 Class 1 Sequence 2 Class 2 Sequence n Class n Pattern 2Pattern 3 … Pattern Library Classification vi more ls … Sequence to classify Compare_ Patterns … On-Line Sequence Classification ONLINE SEQUENCE CLASS. Library Creation Pattern to classify TestingTrie Testing Trie Pattern 1 Compare_ Patterns ClassTrie Class Trie 16

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition. 17 pwd [3] [5.1] vi [1] [5.1] [4.3] who [1] [4.3] vi [2] [3.5] who [2] [3.5] Root Classification – Comparing Process Class TrieTesting Trie pwd [3] [7.1] vi [1] [7.1] [ 7.3] pwd [1] [ 7.3] vi [2] [1.5] pwd [2] [1.5] [0.3] ls [1] [0.3] ls [2] Root … If the node (and its prefix) are in both Tries: If ( abs(chi 2 TestingTrie – chi 2 ClassTrie ) ≤ ThresholdValue ): Similarity Similarity between both tries. Chi 2 TestingTrie Result  [Element TestingTrie, Prefix TestingTrie, Chi 2 TestingTrie ]

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition. 17 pwd [3] [5.1] vi [1] [5.1] [4.3] who [1] [4.3] vi [2] [3.5] who [2] [3.5] Root Classification – Comparing Process Class TrieTesting Trie pwd [3] [7.1] vi [1] [7.1] [ 7.3] pwd [1] [ 7.3] vi [2] [1.5] pwd [2] [1.5] [0.3] ls [1] [0.3] ls [2] Root … If the node (and its prefix) are in both Tries: If (abs(5.1 – 7.1) ≤ ThresholdValue ): Similarity Similarity between both tries. 5.1 Result  [vi, pwd, 5.1]

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition. 17 pwd [3] [5.1] vi [1] [5.1] [4.3] who [1] [4.3] vi [2] [3.5] who [2] [3.5] Root Classification – Comparing Process Class TrieTesting Trie pwd [3] [7.1] vi [1] [7.1] [ 7.3] pwd [1] [ 7.3] vi [2] [1.5] pwd [2] [1.5] [0.3] ls [1] [0.3] ls [2] Root … If the node (and its prefix) are only in the Testing Trie: Difference Difference between both tries. Result  [Element TestingTrie, Prefix TestingTrie, (Chi 2 TestingTrie * -1)] Result  [Element TestingTrie, Prefix TestingTrie, (Chi 2 TestingTrie * -1)]

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition. 17 pwd [3] [5.1] vi [1] [5.1] [4.3] who [1] [4.3] vi [2] [3.5] who [2] [3.5] Root Classification – Comparing Process Class TrieTesting Trie pwd [3] [7.1] vi [1] [7.1] [ 7.3] pwd [1] [ 7.3] vi [2] [1.5] pwd [2] [1.5] [0.3] ls [1] [0.3] ls [2] Root … If the node (and its prefix) are only in the Testing Trie: Difference Difference between both tries. Result  [who, pwd  vi, (-4.3)] Result  [who, pwd  vi, (-4.3)]

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition. 17 pwd [3] [5.1] vi [1] [5.1] [4.3] who [1] [4.3] vi [2] [3.5] who [2] [3.5] Root Classification – Comparing Process Class TrieTesting Trie pwd [3] [7.1] vi [1] [7.1] [ 7.3] pwd [1] [ 7.3] vi [2] [1.5] pwd [2] [1.5] [0.3] ls [1] [0.3] ls [2] Root … If the node (and its prefix) are only in the Testing Trie: Difference Difference between both tries. Result  [who, vi, (-3.5)] Result  [who, vi, (-3.5)]

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Result Result: Value 1 [Element 1, Prefix 1, Value 1 ] Value 2 [Element 2, Prefix 2, Value 2 ] Value 3 [Element 3, Prefix 3, Value 3 ] Value 4 [Element 4, Prefix 4, Value 4 ] … Value n [Element n, Prefix n, Value n ] Each comparison (ClassTrie, TestingTrie): A comparision value. Comparison Value 18 Classification – Comparing Process

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Result Result: + 5.1 [vi, pwd, + 5.1] - 4.3 [who, pwd  vi, - 4.3] - 3.5 [who, pwd, - 3.5]. - 2.7 18 Classification – Comparing Process Comparison Value

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Classification pwd fs fg … vi man ls … … finger more ls... Sequence 1 Class 1 Sequence 2 Class 2 Sequence n Class n Pattern 1Pattern 2Pattern 3 … Pattern Library Library CreationClassification vi more ls … Pattern to classify Sequence to classify … ONLINE SEQUENCE CLASS On-Line Sequence Classification Compare_ Patterns comparision value. 19

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition comparision value Classification pwd fs fg … vi man ls … … finger more ls... Sequence 1 Class 1 Sequence 2 Class 2 Sequence n Class n Pattern 1Pattern 2Pattern 3 … Pattern Library Library CreationClassification vi more ls … Pattern to classify Sequence to classify Compare_ Patterns … ONLINE SEQUENCE CLASS On-Line Sequence Classification Greatest Comparison Value. 20

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Motivation and Introduction Sequence classification Our approach L ibrary Creation Classification Target Environment  Target Environment  Description  Experiments & Results  Conclusions and Future Works 21 Outline

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Environment – UNIX command line sequences # Start session 1 cd ~/private/docs ls -laF | more cat foo.txt bar.txt zorch.txt > a.txt exit # End session 1 # Start session 2 cd ~/games/ xquake & fg …**SOF** cd ls -laF | more cat > exit**EOF** … one "file name" argument three "file name" arguments one "file name" argument UNIX computer users Command histories of 9 UNIX computer users at over 2 years UCI Repository of ML Database [Newman C., Hettich S., Merz, C. (1998)] 22

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Motivation and Introduction Sequence classification Our approach L ibrary Creation Classification Target Environment  Target Environment  Description  Experiments & Results  Conclusions and Future Works 23 Outline

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition 9 files (users) containing from about 10.000 to 60.000 commands each. 1. Extracting Patterns: 1. Extracting Patterns: A trie is created for each user  Pattern Library Experiments – UNIX command line sequences. 24

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition 9 files (users) containing from about 10.000 to 60.000 commands each. 1. Extracting Patterns: 1. Extracting Patterns: A trie is created for each user  Pattern Library Experiments – UNIX command line sequences. 24 2. Classification Algorithm: Sequence to classify (sequences of very different sizes)   Classified in the class with the greatest value (result value).

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition 9 files (users) containing from about 10.000 to 60.000 commands each. 1. Extracting Patterns: 1. Extracting Patterns: A trie is created for each user  Pattern Library Experiments – UNIX command line sequences. 24 2. Classification Algorithm: Sequence to classify (sequences of very different sizes)   Classified in the class with the greatest value (result value). 3. Evaluating the result: Calculate: (+) difference between the greatest value and the second greatest value (+) (-) x difference between the real classification value and the greatest value (-) (The greater the difference, the better the classification)

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Results – UNIX command line sequences Unix Commands Classification – User 6. average of 25 simulation results 25 Classification Value Length of the Sequence to classify

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Results – UNIX command line sequences Minimum length for classifying a UNIX Computer User correctly. 26 Unix Computer User (Class) Length of the Sequence to classify

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Motivation and Introduction Sequence classification Our approach Library Creation Classification Target Environment Description Experiments & Results Conclusions and Future Works  Conclusions and Future Works 27 Outline

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition  A threshold must be found  Long time for creating the tries  Results depend on the length of the sub-sequences used to create the trie Conclusions 28

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Effective method to classify UNIX users If a behavior can be represented by sequences, the proposed classification method can be used If a new class is added, only its trie must be created (the others are not modified) This method could be used for other tasks: sequence prediction, sequence clustering… RoboCup Coach 2006 Competition (succesfully results) Conclusions 29

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition OPattern Library  One Trie for all classes (users). OClassification method without threshold value OAnalysis comparing our approach to others (HMMs) Future Works 30

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Sequence Classification Using Statistical Pattern Recognition José Antonio Iglesias, Agapito Ledezma, and Araceli Sanchis Computer Science Department Universidad Carlos III de Madrid Avda. de la Universidad, 30. 28911 Leganés, Spain {jiglesia, ledezma, masm}@inf.uc3m.es. Thank you!

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Sequence Classification Using Statistical Pattern Recognition José Antonio Iglesias, Agapito Ledezma, and Araceli Sanchis Computer Science Department Universidad Carlos III de Madrid Avda. de la Universidad, 30. 28911 Leganés, Spain {jiglesia, ledezma, masm}@inf.uc3m.es.Questions

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Sequence Classification Using Statistical Pattern Recognition José Antonio Iglesias, Agapito Ledezma, and Araceli Sanchis Computer Science Department Universidad Carlos III de Madrid Avda. de la Universidad, 30. 28911 Leganés, Spain { jiglesia, ledezma, masm}@inf.uc3m.es. Related to Questions... 29

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Experiments – UNIX command line sequences **SOF** cd ls -laF | more cat > … Pattern/Class User0 **SOF** ls exit ls -laF xquake & fg … **SOF** vi vi ls -la cat … USER 0 Class0 USER 1 Class1 USER 8 Class8 … … Pattern Library **SOF** ls -laF | More cd … Test User Sequence Classification Pattern/Class User1 Pattern/Class User8 User On-Line Є Class c User On-Line vs Class User0  21 User On-Line vs Class User1  49 User On-Line vs Class User2  9 User On-Line vs Class User3  3 User On-Line vs Class User4  12 User On-Line vs Class User5  29 User On-Line vs Class User6  -1 User On-Line vs Class User7  0 User On-Line vs Class User8  11 ClassUser1

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Experiments – UNIX command line sequences **SOF** cd ls -laF | more cat > … Pattern/Class User0 **SOF** ls exit ls -laF xquake & fg … **SOF** vi vi ls -la cat … USER 0 Class0 USER 1 Class1 USER 8 Class8 … … Pattern Library **SOF** ls -laF | More cd … Test User Sequence Classification Pattern/Class User1 Pattern/Class User8 User On-Line Є Class c User On-Line vs Class User0  21 User On-Line vs Class User1  49 User On-Line vs Class User2  9 User On-Line vs Class User3  3 User On-Line vs Class User4  12 User On-Line vs Class User5  29 User On-Line vs Class User6  -1 User On-Line vs Class User7  0 User On-Line vs Class User8  11 ClassUser1 Correctly Classified

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Experiments – UNIX command line sequences **SOF** cd ls -laF | more cat > … Pattern/Class User0 **SOF** ls exit ls -laF xquake & fg … **SOF** vi vi ls -la cat … USER 0 Class0 USER 1 Class1 USER 8 Class8 … … Pattern Library **SOF** ls -laF | More cd … Test User Sequence Classification Pattern/Class User1 Pattern/Class User8 User On-Line Є Class c User On-Line vs Class User0  21 User On-Line vs Class User1  49 User On-Line vs Class User2  9 User On-Line vs Class User3  3 User On-Line vs Class User4  12 User On-Line vs Class User5  29 User On-Line vs Class User6  -1 User On-Line vs Class User7  0 User On-Line vs Class User8  11 ClassUser1 Correctly Classified 20

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Experiments – UNIX command line sequences **SOF** cd ls -laF | more cat > … Pattern/Class User0 **SOF** ls exit ls -laF xquake & fg … **SOF** vi vi ls -la cat … USER 0 Class0 USER 1 Class1 USER 8 Class8 … … Pattern Library **SOF** ls -laF | More cd … Test User Sequence Classification Pattern/Class User1 Pattern/Class User8 User On-Line Є Class c User On-Line vs Class User0  21 User On-Line vs Class User1  49 User On-Line vs Class User2  9 User On-Line vs Class User3  3 User On-Line vs Class User4  12 User On-Line vs Class User5  29 User On-Line vs Class User6  -1 User On-Line vs Class User7  0 User On-Line vs Class User8  11 ClassUser2

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Experiments – UNIX command line sequences **SOF** cd ls -laF | more cat > … Pattern/Class User0 **SOF** ls exit ls -laF xquake & fg … **SOF** vi vi ls -la cat … USER 0 Class0 USER 1 Class1 USER 8 Class8 … … Pattern Library **SOF** ls -laF | More cd … Test User Sequence Classification Pattern/Class User1 Pattern/Class User8 User On-Line Є Class c User On-Line vs Class User0  21 User On-Line vs Class User1  49 User On-Line vs Class User2  9 User On-Line vs Class User3  3 User On-Line vs Class User4  12 User On-Line vs Class User5  29 User On-Line vs Class User6  -1 User On-Line vs Class User7  0 User On-Line vs Class User8  11 NO Correctly Classified ClassUser2

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Experiments – UNIX command line sequences **SOF** cd ls -laF | more cat > … Pattern/Class User0 **SOF** ls exit ls -laF xquake & fg … **SOF** vi vi ls -la cat … USER 0 Class0 USER 1 Class1 USER 8 Class8 … … Pattern Library **SOF** ls -laF | More cd … Test User Sequence Classification Pattern/Class User1 Pattern/Class User8 User On-Line Є Class c User On-Line vs Class User0  21 User On-Line vs Class User1  49 User On-Line vs Class User2  9 User On-Line vs Class User3  3 User On-Line vs Class User4  12 User On-Line vs Class User5  29 User On-Line vs Class User6  -1 User On-Line vs Class User7  0 User On-Line vs Class User8  11 NO Correctly Classified - 40 ClassUser2

José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Sequence Classification Using.

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José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Sequence Classification Using.

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