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Pattern Recognition Lab Assignment help tutors help with topics like Pattern recognition algorithms, both supervised and unsupervised maching learning algorithms,constraints of a distributed embedded system puts on the algorithms ,Healthcare applications of a pattern recognition system 

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Pattern Recognition topics include :
  • pattern recognition, Feature detection, Probability theory, Conditional probability and bayes rule, Random vectors, expectation, correlation, covariance, Linear transformations, Decision theory
  • Roc curves, Likelihood ratio test, Linear and quadratic discriminants, Fisher discriminant, Sufficient statistics, Coping with missing or noisy features.
  • Template-based recognition, feature extraction, Eigenvector and multilinear analysis, Maximum likelihood and bayesian parameter estimation, Linear discriminant/perceptron learning
  • Optimization by gradient descent, Support vector machines, K-nearest-neighbor classification, Non-parametric classification, Density estimation, Parzen estimation, Clustering, vector quantization
  • k-means, Mixture modeling, expectation-maximization, Hidden markov models.
  • Viterbi algorithm, Baum-welch algorithm, Linear dynamical systems, Bayesian networks, Decision trees, multi-layer perceptrons, Genetic algorithms, Combination of multiple classifiers
  • pattern recognition, Bayesian decision theory, Bayesian estimation, Gaussian distribution, Ml estimation, Em algorithm, Feature selection and extraction, Linear discriminant functions
  • Nonparametric pattern recognition, Algorithm-independent learning, Comparing classifiers, Learning with multiple algorithms, Syntactic pattern recognition
  • Decision Trees: CART, C4.5, ID3. Random Forests, Bayesian Decision Theory Grounding our inquiry, Linear Discriminants Discriminative Classifiers: the Decision Boundary, Separability, Perceptrons, Support Vector Machines

  • Parametric Techniques Generative Methods grounded in Bayesian Decision Theory, Maximum Likelihood Estimation, Bayesian Parameter Estimation, Sufficient Statistics

  • Non-Parametric Techniques: Kernel Density Estimators, Parzen Window, Nearest Neighbor Methods, Unsupervised Methods Exploring the Data for Latent Structure, Component Analysis and Dimension Reduction

  • Curse of Dimensionality, Principal Component Analysis, Fisher Linear Discriminant, Locally Linear Embedding, Clustering: K-Means, Expectation Maximization, Mean Shift
    Classifier Ensembles: Bagging, Boosting / AdaBoost

  • Graphical Models The Modern Language of Pattern Recognition and Machine Learning: Bayesian Networks, Sequential Models: State-Space Models, Hidden Markov Models, Dynamic Bayesian Networks

  • No Free Lunch Theorem, Ugly Duckling Theorem, Bias-Variance Dilemma, Jacknife and Bootstrap Methods, Other Items Time Permitting: Syntactic Methods, Neural Networks

  • Statistical Methods,Non-Parametric Techniques ,Linear and Piecewise-Linear Discriminate Design,Automata Theory and Formal Languages,Grammatical Inference,Artificial Neural Networks
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