The document provides an overview of various machine learning classification algorithms including decision trees, lazy learners like K-nearest neighbors, decision lists, naive Bayes, artificial neural networks, and support vector machines. It also discusses evaluating and combining classifiers, as well as preprocessing techniques like feature selection and dimensionality reduction.
Machine Learning and Data Mining: 13 Nearest Neighbor and Bayesian ClassifiersPier Luca Lanzi
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. This lecture introduces nearest neighbor and Bayesian classifiers
Slides were formed by referring to the text Machine Learning by Tom M Mitchelle (Mc Graw Hill, Indian Edition) and by referring to Video tutorials on NPTEL
Machine Learning and Data Mining: 13 Nearest Neighbor and Bayesian ClassifiersPier Luca Lanzi
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. This lecture introduces nearest neighbor and Bayesian classifiers
Slides were formed by referring to the text Machine Learning by Tom M Mitchelle (Mc Graw Hill, Indian Edition) and by referring to Video tutorials on NPTEL
Machine learning in science and industry — day 2arogozhnikov
- decision trees
- random forest
- Boosting: adaboost
- reweighting with boosting
- gradient boosting
- learning to rank with gradient boosting
- multiclass classification
- trigger in LHCb
- boosting to uniformity and flatness loss
- particle identification
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...Marina Santini
In this lecture, we talk about two different discriminative machine learning methods: decision trees and k-nearest neighbors. Decision trees are hierarchical structures.k-nearest neighbors are based on two principles: recollection and resemblance.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
Tutorial presented at ACM SIGIR/SIGKDD Africa Summer School on Machine Learning for Data Mining and Search (AFIRM 2020) conference in Cape Town, South Africa.
[Paper reading] L-SHAPLEY AND C-SHAPLEY: EFFICIENT MODEL INTERPRETATION FOR S...Daiki Tanaka
paper at ICML 2019; "L-SHAPLEY AND C-SHAPLEY: EFFICIENT MODEL INTERPRETATION FOR STRUCTURED DATA"
openr eview link : https://openreview.net/forum?id=S1E3Ko09F7
* ML in HEP
* classification and regression
* knn classification and regression
* ROC curve
* optimal bayesian classifier
* Fisher's QDA
* intro to Logistic Regression
Learning to rank (LTR) for information retrieval (IR) involves the application of machine learning models to rank artifacts, such as items to be recommended, in response to user's need. LTR models typically employ training data, such as human relevance labels and click data, to discriminatively train towards an IR objective. The focus of this tutorial will be on the fundamentals of neural networks and their applications to learning to rank.
ATP Blog06 - Review Of Humic Substances By - Pena Mendezatpcorporation
A T P Blog06 - Review Of Humic Substances By - Pena,Mendez, Havel & Patocka.
You may want to print the various FULVIC - Humic Acid Report and other educational documents that we have and give it them to your doctor.
Your doctor may very well be grateful that you did......
Machine learning in science and industry — day 2arogozhnikov
- decision trees
- random forest
- Boosting: adaboost
- reweighting with boosting
- gradient boosting
- learning to rank with gradient boosting
- multiclass classification
- trigger in LHCb
- boosting to uniformity and flatness loss
- particle identification
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...Marina Santini
In this lecture, we talk about two different discriminative machine learning methods: decision trees and k-nearest neighbors. Decision trees are hierarchical structures.k-nearest neighbors are based on two principles: recollection and resemblance.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
Tutorial presented at ACM SIGIR/SIGKDD Africa Summer School on Machine Learning for Data Mining and Search (AFIRM 2020) conference in Cape Town, South Africa.
[Paper reading] L-SHAPLEY AND C-SHAPLEY: EFFICIENT MODEL INTERPRETATION FOR S...Daiki Tanaka
paper at ICML 2019; "L-SHAPLEY AND C-SHAPLEY: EFFICIENT MODEL INTERPRETATION FOR STRUCTURED DATA"
openr eview link : https://openreview.net/forum?id=S1E3Ko09F7
* ML in HEP
* classification and regression
* knn classification and regression
* ROC curve
* optimal bayesian classifier
* Fisher's QDA
* intro to Logistic Regression
Learning to rank (LTR) for information retrieval (IR) involves the application of machine learning models to rank artifacts, such as items to be recommended, in response to user's need. LTR models typically employ training data, such as human relevance labels and click data, to discriminatively train towards an IR objective. The focus of this tutorial will be on the fundamentals of neural networks and their applications to learning to rank.
ATP Blog06 - Review Of Humic Substances By - Pena Mendezatpcorporation
A T P Blog06 - Review Of Humic Substances By - Pena,Mendez, Havel & Patocka.
You may want to print the various FULVIC - Humic Acid Report and other educational documents that we have and give it them to your doctor.
Your doctor may very well be grateful that you did......
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Introduction to machine learning terminology.
Applications within High Energy Physics and outside HEP.
* Basic problems: classification and regression.
* Nearest neighbours approach and spacial indices
* Overfitting (intro)
* Curse of dimensionality
* ROC curve, ROC AUC
* Bayes optimal classifier
* Density estimation: KDE and histograms
* Parametric density estimation
* Mixtures for density estimation and EM algorithm
* Generative approach vs discriminative approach
* Linear decision rule, intro to logistic regression
* Linear regression
3D Scene Analysis via Sequenced Predictions over Points and RegionsFlavia Grosan
I gave this talk in Machine Vision seminar at Jacobs University. I presented the state of the art in 3D point cloud classification and I described X. Xiong et al approach in a paper published in 2010.
Lecture 10b: Classification. k-Nearest Neighbor classifier, Logistic Regression, Support Vector Machines (SVM), Naive Bayes (ppt,pdf)
Chapters 4,5 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar.
1. All images from wikimedia commons, a freely-licensed media repository
2. “ Classifiers” R & D project by Aditya M Joshi [email_address] IIT Bombay Under the guidance of Prof. Pushpak Bhattacharyya [email_address] IIT Bombay
5. What is classification? A machine learning task that deals with identifying the class to which an instance belongs A classifier performs classification Classifier Test instance Attributes (a1, a2,… an) Discrete-valued Class label ( Age, Marital status, Health status, Salary ) Issue Loan? {Yes, No} ( Perceptive inputs ) Steer? { Left, Straight, Right } Category of document? {Politics, Movies, Biology} ( Textual features : Ngrams )
6. Classification learning Training phase Testing phase Learning the classifier from the available data ‘Training set’ (Labeled) Testing how well the classifier performs ‘Testing set’
10. Diagram from Han-Kamber Example tree Intermediate nodes : Attributes Leaf nodes : Class predictions Edges : Attribute value tests Example algorithms: ID3, C4.5, SPRINT, CART
11. Decision Tree schematic Training data set a1 a2 a3 a4 a5 a6 a1 a2 a3 a4 a5 a6 X Y Z Pure node, Leaf node: Class RED Impure node, Select best attribute and continue Impure node, Select best attribute and continue
20. Decision List learning R S’ = S Set of candidate feature functions For each hi, Qi = Pi U Ni ( hi = 1 ) U i = max { | Pi| - pn * | Ni | , |Ni| - pp *|Pi| } Select hk, the feature with highest utility ( h k, ) If (| Pi| - pn * | Ni | > |Ni| - pp *|Pi| ) then 1 else 0 1 / 0 - Qk
41. SVM Issues SVMs are immune to the removal of non-support-vector points What if n-classes are to be predicted? Problem : SVMs deal with two-class classification Solution : Have multiple SVMs each for one class
44. Bagging Total set Sample D 1 Classifier model M 1 At random. May use bootstrap sampling with replacement Training dataset D Classifier learning scheme Classifier model M n Test set Majority vote Class Label
45. Boosting (AdaBoost) Total set Sample D 1 Classifier model M 1 Selection based on weight. May use bootstrap sampling with replacement Training dataset D Classifier learning scheme Classifier model M n Test set Weighted vote Class Label Initialize weights of instances to 1/d Weights of correctly classified instances multiplied by error / (1 – error) If error > 0.5? Error Error `
54. Parts of weka Explorer Basic interface to run ML Algorithms Experimenter Comparing experiments on different algorithms Knowledge Flow Similar to Work Flow ‘ Customized’ to one’s needs
![“ Classifiers” R & D project by Aditya M Joshi [email_address] IIT Bombay Under the guidance of Prof. Pushpak Bhattacharyya [email_address] IIT Bombay](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)