'Class labels' presentation slideshows

Cluster validation

1.71k views • 94 slides

Spatial and Temporal Data Mining

Spatial and Temporal Data Mining. Classification and Prediction. Vasileios Megalooikonomou. (based on notes by Jiawei Han and Micheline Kamber). Agenda. What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction

1.26k views • 92 slides

CIS664-Knowledge Discovery and Data Mining

CIS664-Knowledge Discovery and Data Mining. Classification and Prediction. Vasileios Megalooikonomou Dept. of Computer and Information Sciences Temple University. (based on notes by Jiawei Han and Micheline Kamber). Agenda. What is classification? What is prediction?

1.04k views • 92 slides

944 views • 78 slides

Midterm 3 Revision and ID3

903 views • 78 slides

Lecture 7 Classification

Lecture 7 Classification. ITCS 6163. Chapter 7. Classification and Prediction. What is classification? What is prediction? Classification by decision tree induction Bayesian Classification Other Classification Methods (SVM) Classification accuracy Prediction Summary.

1.22k views • 70 slides

Lecture 7 Classification

Lecture 7 Classification. ITCS 6163. Chapter 7. Classification and Prediction. What is classification? What is prediction? Classification by decision tree induction Bayesian Classification Other Classification Methods (SVM) Classification accuracy Prediction Summary.

976 views • 68 slides

Lecture 4: Logistic Regression

Lecture 4: Logistic Regression. Machine Learning CUNY Graduate Center. Today. Linear Regression Bayesians v. Frequentists Bayesian Linear Regression Logistic Regression Linear Model for Classification. Regularization: Penalize large weights. Regularized Regression

952 views • 54 slides

624 views • 49 slides

Data Mining

Data Mining. Instructor: Bajuna Salehe Email: bajunar@yahoo.com Web: http://www.ifm.ac.tz/staff/bajuna/courses. Classification and Prediction. Classification and Prediction.

551 views • 44 slides

Monday, 10 March 2008 William H. Hsu Department of Computing and Information Sciences, KSU

514 views • 41 slides

Ensemble Classifiers

Ensemble Classifiers. Prof. Navneet Goyal. Ensemble Classifiers. Introduction & Motivation Construction of Ensemble Classifiers Boosting (Ada Boost) Bagging Random Forests Empirical Comparison. Introduction & Motivation. Suppose that you are a patient with a set of symptoms

781 views • 37 slides

Part III The Nearest-Neighbor Classifier

Part III The Nearest-Neighbor Classifier. NASA Space Program. Outline. Training and test data accuracy The nearest-neighbor classifier. Classification Accuracy. Say we have N feature vectors Say we know the true class label for each feature vector

653 views • 31 slides

Linear Separators

Linear Separators. Bankruptcy example. R is the ratio of earnings to expenses L is the number of late payments on credit cards over the past year. We would like here to draw a linear separator , and get so a classifier. 1-Nearest Neighbor Boundary.

412 views • 25 slides

On Compression-Based Text Classification

On Compression-Based Text Classification. Yuval Marton 1 , Ning Wu 2 and Lisa Hellerstein 2 1) University of Maryland and 2) Polytechnic University. ECIR-05 Santiago de Compostela, Spain. March 2005. Compression for Text Classification??.

341 views • 23 slides

Mining Relationships Among Interval-based Events for Classification

Mining Relationships Among Interval-based Events for Classification. Dhaval Patel ? Wynne Hsu Mong ? Li Lee SIGMOD 08. Outline. Introduction Preliminaries Augment hierarchical representation Interval-based event mining Interval-based event classifier Experiment Conclusion.

441 views • 19 slides

Classification and Regression

Classification. Classification and Regression. Classification: predicts categorical class labels classifies data (constructs a model) based on the training set and the values ( class labels ) in a classifying attribute and uses it in classifying new data Regression:

400 views • 15 slides

Analysis of Social Media MLD 10-802, LTI 11-772

223 views • 12 slides

Low Dimensional Representations And Multidimensional Scaling (MDS ) (Sec 10.14)

206 views • 6 slides

A scalable Approach to Size-independent Network Similarity

A scalable Approach to Size-independent Network Similarity. MOTIVATION. Anomaly detection: different models?. 1. REQUIREMENTS P1. effectiveness size-independence intuitiveness interpretability P2. scalability. Discontinuity Detection. 2. Day 1 Day 2 Day 3 Day 4.

211 views • 2 slides