Outline Intro to Representation and Heuristic Search Machine Learning (Clustering) and My Research.
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Transcript of Outline Intro to Representation and Heuristic Search Machine Learning (Clustering) and My Research.
![Page 1: Outline Intro to Representation and Heuristic Search Machine Learning (Clustering) and My Research.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649f505503460f94c72f01/html5/thumbnails/1.jpg)
Outline
Intro to Representation and Heuristic Search
Machine Learning (Clustering) and My Research
![Page 2: Outline Intro to Representation and Heuristic Search Machine Learning (Clustering) and My Research.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649f505503460f94c72f01/html5/thumbnails/2.jpg)
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
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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…
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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
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“tic-tac-toe” state space graph
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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
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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
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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
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Introduction
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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
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Introduction
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Introduction
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Outline
Intro to Representation and Heuristic Search
Machine Learning (Clustering) and My Research
![Page 14: Outline Intro to Representation and Heuristic Search Machine Learning (Clustering) and My Research.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649f505503460f94c72f01/html5/thumbnails/14.jpg)
Clustering
Clustering is trying to find similar groups based on given dimensions
It is know as unsupervised learning
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K-means Clustering
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K-means Clustering
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K-means Clustering
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K-means Clustering
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K-means Clustering
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Experiment setup: HSSP matrix: 1b25
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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
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HSSP-BLOSUM62 Measure
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Research Topics
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Part1Bioinformatics
Knowledge and Dataset Collection
Part2Discovering Protein
Sequence Motifs
Part3Motif Information
Extraction
Part4Mining the Relations between Motifs and
Motifs
Part5Protein Local Tertiary Structure Prediction
FutureWorks