2.Classification in Artificial Neural Network Examples.ppt

1. Classification Examples
Classification Examples

2. Classification Examples
Classification Examples
 Classification has many applications in real life.
 From last lecture:
 Weather Prediction
 Image Classification
 Document Classification
 More examples next !

3. Face Recognition
Face Recognition
 Input : an image
 Output: Specifying the location of the face (if exists)
 More advanced: A specific person face recognition.

4. Road Sign Recognition
Road Sign Recognition
 Input : an image of a road sign (taken by a driverless car)
 Output: recognizing the sign

5. Tumor Recognition
Tumor Recognition
 Input : an MRI image of the patient.
 Output: recognizing if it has tumor or not.

6. Handwriting Recognition
Handwriting Recognition
 Input : an image that contains handwriting.
 Output: recognizing the handwriting.

7. Medical Diagnosis
Medical Diagnosis
Training
Testing
Temp Tired? Cough? Smell … Corona?
27 Y Y N Y
39 Y Y Y Y
27 N N Y N
31 Y N N ??

8. Sentiment Analysis
Sentiment Analysis
 Classifying people feelings toward certain product,
decision, topic, …
Training
“ This restaurant is awesome “ , label = positive
“ I do not like this car” , label = negative
…
Testing
“ This phone is just amazing “ , label = ?
 Can be used by companies to evaluate and improve their
products using the reviews.

9. Classifiers
Classifiers
 A classifier is a method that learns from training data to predict
the labels of testing data.
 There are many classifiers, each classifier has its own method.
 Different classifiers have different accuracies.

10. Classifiers
Classifiers
 Examples of classifiers:
 K Nearest Neighbor (KNN)
 Decision Trees
 Logistic Regression
 Artifical Neural Networks (ANN)
 Support Vector Machines (SVM)
 Rule-Based Classifiers
 Bayesian Classifiers

11. Selection of Classifiers
Selection of Classifiers
 There are many classifiers out there, which classifier should I
use ?
 It depends on the problem.
 Each classifier has its own advantages and disadvantages.
 For fast training time, we use Naïve Bayes or KNN, we do not
use decision tree or ANN
 For high accuracy, we use ANN or SVM
 For fast prediction, we use decision tree, not KNN
 Dataset is small, do not use ANN
 Dataset is very large, use ANN, do not use SVM