PPT-Classification / Regression

Author : liane-varnes | Published Date : 2016-07-13

Neural Networks 2 Neural networks Topics Perceptrons structure training expressiveness Multilayer networks possible structures activation functions training with

Presentation Embed Code

Download Presentation

Download Presentation The PPT/PDF document "Classification / Regression" is the property of its rightful owner. Permission is granted to download and print the materials on this website for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

Classification / Regression: Transcript

Neural Networks 2 Neural networks Topics Perceptrons structure training expressiveness Multilayer networks possible structures activation functions training with gradient descent and . Chris Franck. LISA Short Course. March 26, 2013. Outline. Overview of LISA. Overview of CART. Classification tree description. Examples – iris and skull data.. Regression tree description. Examples – simulated and car data. L. ou, Rich . Caruana. , Johannes . Gehrke. (Cornell University). KDD, 2012. Presented by: . Haotian. Jiang. 3.31.2015. Intelligible Models for Classification and Regression. Motivation. 2. . G. eneralized Additive Model. Leukaemia. Challenge. AGCT meeting, August 2011. David . Amar. , . Yaron. Orenstein & Ron . Zeira. Ron Shamir’s group. http://www.the-dream-project.org/challanges/dream6flowcap2-molecular-classification-acute-myeloid-leukaemia-challenge. itation. Feb. 5, 2015. Outline. Linear regression. Regression: predicting a continuous value. Logistic regression. Classification: predicting a discrete value. Gradient descent. Very general optimization technique. [slides prises du cours cs294-10 UC Berkeley (2006 / 2009)]. http://www.cs.berkeley.edu/~jordan/courses/294-fall09. Basic Classification in ML. !!!!$$$!!!!. Spam . filtering. Character. recognition. Input . Overview of Supervised Learning. Outline. Regression vs. Classification. Two . Basic Methods: Linear Least Square vs. Nearest Neighbors. C. lassification via Regression. C. urse of Dimensionality and . Weifeng Li, Sagar . Samtani. and . Hsinchun. . Chen. Spring 2016. Acknowledgements:. Cynthia . Rudin. , Hastie & . Tibshirani. Michael Crawford – San Jose State University. Pier Luca . Lanzi. HUDK5199. Spring term, . 2013. February . 18, 2013. Today’s Class. Classification. And then some discussion of features in Excel between end of class and 5pm. We will start today, and continue in future classes as needed. Ryan Shaun . Joazeiro. de Baker. Prediction. Pretty much what it says. A student is using a tutor right now.. Is he gaming the system or not?. . (“attempting to succeed in an interactive learning environment by exploiting properties of the system rather than by learning the material”). 12/8/16. BGU, DNN course 2016. Sources. Main paper. “. Rich . feature hierarchies for accurate object detection and semantic . segmentation. ”, . Ross . Girshick. , Jeff Donahue, Trevor Darrell, . Dan Jurafsky. Stanford University . Logistic Regression. Logistic Regression. Important analytic tool in natural and social sciences. Baseline supervised machine learning tool for classification. Is also the foundation of a neural network. Machine Learning. Classification. Email: Spam / Not Spam?. Online Transactions: Fraudulent (Yes / No)?. Tumor: Malignant / Benign ?. 0: “Negative Class” (e.g., benign tumor). . 1: “Positive Class” (e.g., malignant tumor). 2. Dr. Alok Kumar. Logistic regression applications. Dr. Alok Kumar. 3. When is logistic regression suitable. Dr. Alok Kumar. 4. Question. Which of the following sentences are . TRUE. about . Logistic Regression. Linear regression, . k-. NN classification. Debapriyo Majumdar. Data Mining – Fall 2014. Indian Statistical Institute Kolkata. August 11, 2014. An Example: Size of Engine . vs. Power. 2. Engine displacement (cc).

Download Document

Here is the link to download the presentation.
"Classification / Regression"The content belongs to its owner. You may download and print it for personal use, without modification, and keep all copyright notices. By downloading, you agree to these terms.