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Outline Intro to Representation and Heuristic Search Machine Learning (Clustering) and My Research Introduction to Representation The representation function is to ... – PowerPoint PPT presentation

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Title: Outline


1
Outline
  • Intro to Representation and Heuristic Search
  • Machine Learning (Clustering) and My Research

2
Introduction to Representation
  • The representation function is to capture the
    critical features of a problem and make that
    information accessible to a problem solving
    procedure
  • Expressiveness (the result of the feature
    abstracted) and efficiency (the computational
    complexity) are major dimensions for evaluating
    knowledge representation

3
Introduction to Search
  • Consider tic-tac-toe
  • Starting with an empty board,
  • The first player can place a X on any one of nine
    places
  • Each move yields a different board that will
    allow the opponent 8 possible responses
  • and so on

4
Introduction to Search
  • We can represent this collection of possible
    moves by regarding each board as a state in a
    graph
  • The link of the graph represent legal move
  • The resulting structure is a state space graph

5
tic-tac-toe state space graph
6
Introduction to Search
  • Human use intelligent search
  • Human do not do exhaustive search
  • The rules are known as heuristics, and they
    constitute one of the central topics of AI search

7
State Space Representation
  • State space search characterizes problem solving
    as the process of finding a solution path form
    the start state to a goal
  • A goal may describe a state, such as winning
    board in tic-tac-toe

8
Introduction
  • Consider heuristic in the game of tic-tac-toe
  • A simple analysis put the total number of states
    for 9!
  • Symmetry reduction decrease the search space
  • Thus, there are not 9 but 3 initial moves
  • to a corner
  • to the center of a side
  • to the center of the grid

9
Introduction
10
Introduction
  • Use of symmetry on the second level further
    reduces the number of path to 3 12 7!
  • A simple heuristic, can almost eliminate search
    entirely we may move to the state in which X has
    the most winning opportunity
  • In this case, X takes the center of the grid as
    the first step

11
Introduction
12
Introduction
13
Outline
  • Intro to Representation and Heuristic Search
  • Machine Learning (Clustering) and My Research

14
Clustering
  • Clustering is trying to find similar groups based
    on given dimensions
  • It is know as unsupervised learning

15
K-means Clustering
16
K-means Clustering
17
K-means Clustering
18
K-means Clustering
19
K-means Clustering
20
Experiment setup HSSP matrix 1b25
21
Representation of Segment
  • Sliding window size 9
  • Each window corresponds to a sequence segment,
    which is represented by a 9 20 matrix plus
    additional nine corresponding secondary structure
    information obtained from DSSP.
  • More than 560,000 segments (413MB) are generated
    by this method.
  • DSSP Obtain 2nd Structure information

22
HSSP-BLOSUM62 Measure

23
Research Topics
24
FutureWorks
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