• Chapter 10 Hidden Markov ModelsChapter 10 Hidden Markov Models

    Similarly, from the data given in Figure 10.5, we can estimate the parameter values of the HMM corresponding to digit 7. 1 = 0/12 = 0 and 2 = 12/12 = 1 a11 = 4/5, a12 = 1/5, a21 = 6/7 and a22 = 1/7 There are six different observation symbols (rows) in the training patterns given in Figure 10.5. They are “111”, “001”, “011”, “101”, “110”, and...

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  • Chapter 9 Combination of classifiersChapter 9 Combination of classifiers

    An ROC curve is plotted as follows. Starting at the bottom-left hand corner (where the TPF and FPP are both 0), we check the actual class label of the tuple at the top of the list. If we have a true positive then on the ROC curve, we move up and plot a point. If, the tuple really belongs to the ‘no’ class, we have a false positive, on the ROC curv...

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  • Chapter 8 Support Vector MachinesChapter 8 Support Vector Machines

    The quadratic programming differs from the linear programming only in that the objective function also include xj2 and xixj (i  j) terms. If we used matrix notation, the problem is to find x so as to Maximize f(x) = cx – (1/2)xTQx, subject to Ax  b and x  0 where c is a row vector, x and b are column vectors, Q and A are matrices. The qij...

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  • Chapter 5 Decision treesChapter 5 Decision trees

    Pruning If the tree is grown too large, it can be pruned by trimming in a bottom-up fashion. All pairs of neighboring leaf nodes (i.e. ones linked to a common parent) are considered for elimination. Any pair whose elimination results in a satisfactory (small) increase in impurity is eliminated, and the common parent node becomes a leaf node. (Thi...

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  • Chapter 4 Bayes ClassifierChapter 4 Bayes Classifier

    Prior probability: probability in the absence of any other information P(A): probability of event A Example: P(Dice = 2) = 1/6 random variable: Dice domain = <1, 2, 3, 4, 5, 6> probability distribution: P(Dice) = <1/6, 1/6, 1/6, 1/6, 1/6, 1/6>

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  • Chapter 3 Nearest neighbor based classifiersChapter 3 Nearest neighbor based classifiers

    Conclusions The nearest-neighbor classifiers are appealing and effective. The drawback is in the implementation: high computational time and space for large training data. To reduce the computation time, various approaches have been proposed which help retrieve the nearest neighbors in a short time (e.g. using the support of some data structures...

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  • Chapter 2 Pattern RepresentationChapter 2 Pattern Representation

    The Cayley-Hamilton Theorem In practical calculation, we set the characteristic polynomial equal to zero, given the characteristic equation: det |A - I| = 0 The zeros of the characteristic polynomial, which are solutions to this equation, are the eigenvalues of the matrix A. The second step in solving the eigenvalue problem is to find the eig...

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  • Chapter 1 Overview on Pattern Recognition and Machine LearningChapter 1 Overview on Pattern Recognition and Machine Learning

    5. Two paradigms of pattern recognition There are several paradigms which have been used to solve the pattern recognition problem. The two main ones are statistical pattern recognition and syntactic pattern recognition In statistical pattern recognition, we use vector spaces to represent patterns and classes. The abstractions deal with probabili...

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  • Bài giảng Database Systems - Chapter 11: Relational Database Design Algorithms and Further DependenciesBài giảng Database Systems - Chapter 11: Relational Database Design Algorithms and Further Dependencies

    Recap  Designing a Set of Relations  Properties of Relational Decompositions  Algorithms for Relational Database Schema  Multivalued Dependencies and Fourth Normal Form  Join Dependencies and Fifth Normal Form  Inclusion Dependencies  Other Dependencies and Normal Forms

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  • Bài giảng Database Systems - Chapter 10: Functional Dependencies and Normalization for Relational DatabasesBài giảng Database Systems - Chapter 10: Functional Dependencies and Normalization for Relational Databases

    Chapter Outline  Informal Design Guidelines for Relational Databases  Functional Dependencies (FDs)  Definition, Inference Rules, Equivalence of Sets of FDs, Minimal Sets of FDs  Normal Forms Based on Primary Keys  General Normal Form Definitions (For Multiple Keys)  BCNF (Boyce-Codd Normal Form)

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