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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, 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