# CSI 5325 paper presentations

Here we will organize the papers that the students in the class will present. Typically, we will have a presentation on one paper relevant to the topic of discussion shortly after the professor has finished his lectures on the topic.

I will put up a list of potential papers for each topic; it's up to the presenter to choose the paper at least a week prior to the presentation. Let me know which paper you choose. Note that some papers are in postscript format -- you may need to get a postscript reader for these.

Before the presentation, everyone in the class must read the paper so we may have a fruitful discussion. Thus the target audience of the presentation is people who already have learned something about the topic.

## Guidelines and evaluation

The presentation should use slides and be about 20 minutes long, and allow for an additional 10 minutes of discussion (either at the end, or during the talk). As a rule of thumb, 1 minute of presentation means about 1 slide. The presentation should address and lead the class in a discussion of the main points of the article. In particular, talk about:

- the point of the paper
- primary algorithms
- how the ideas are evaluated (e.g. experimental setup)
- the results (including advantages/drawbacks)

Consider this advice from Charles Elkan on notes on giving a research talk

Your grade will be based on the clarity and quality of your presentation, how well you lead the discussion and are able to answer any questions that come up.

## Topic: Decision trees (February 3; Deborah)

Choose from one of the following 5 papers. I recommend the Oliver or Blockeel papers.

Update Fri Jan 23 11:49:35 CST 2009 -- we will be reading the Oliver and Hand paper.

- Efficient algorithms for decision tree cross-validation (Blockeel, Struyf)
- Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria (Drummond, Holte)
- Option Decision Trees with Majority Votes (Kohavi, Kunz)
- The Effects of Training Set Size on Decision Tree Complexity (Oates, Jensen)
- On pruning and averaging decision trees (Oliver, Hand)

## Topic: Neural networks (February 12; Greg)

Choose from one of the following 7 papers. I recommend the Caruana or Dietterich papers.

Update Wed Feb 4 12:11:20 CST 2009 -- we will be reading the Dietterich paper.

- A Neural Probabilistic Language Model (Bengio, Ducharme, Vincent)
- Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping (Caruana, Lawrence, Giles)
- A comparison of ID3 and backpropagation for English text-to-speech mapping (Dietterich, Hild, Bakiri)
- Fast Neural Network Emulation of Dynamical Systems for Computer Animation (Grzeszczuk, Terzopoulos, Hinton)
- Neural Networks for Density Estimation (Magdon-Ismail, Atiya)
- Intrusion Detection with Neural Networks (Ryan, Lin, Mikkulainen)
- Effective Training of a Neural Network Character Classifier for Word Recognition (Yaeger, Lyon, Webb)

## Topic: Learning theory (March 24; Lei)

Choose from one of the following 5 papers.

Update Tue Mar 17 17:44:27 CDT 2009 -- we will be reading the Balcan and Blum paper.

- Teaching randomized learners (Balbach and Zeugmann)
- A PAC-style model for learning from labeled and unlabeled data (Balcan and Blum)
- Learnability and the Vapnik-Chervonenkis dimension (Blumer, Eherenfeucht, Haussler, and Warmuth)
- Quantifying inductive bias: AI learning algorithms and Valiant's learning framework (Haussler)
- A theory of the learnable (Valiant)

## Topic: Instance-based learning (March 31; Vidhi)

Choose from one of the following 7 papers.

Update Thu Mar 19 11:19:54 CDT 2009 -- we will be reading the Quinlan paper.

- An optimal algorithm for approximate nearest neighbor searching (Arya, Mount, Netanyahu, Silverman, and Wu)
- A learning framework for nearest neighbor search (Cayton and Dasgupta)
- Multiresolution instance-based learning (Deng and Moore)
- Approximate nearest neighbors: towards removing the curse of dimensionality (Indyk and Motwani)
- Efficient exact k-nn and nonparametric classification in high dimensions (Liu, Moore, and Gray)
- Efficient algorithms for minimizing cross validation error (Moore and Lee)
- Combining instance-based and model-based learning (Quinlan)

## Topic: Bayesian learning (April 7; Kevin)

Update Fri Feb 20 20:14:19 CST 2009 -- we will be reading the paper by Lowd and Domingos.

- Naive Bayes Models for Probability Estimation (Lowd, Domingos)
- Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier (Domingos, Pazzani)
- NewsWeeder: Learning to Filter Netnews (Lang)
- Convergence rates of the Voting Gibbs classifier with application to Bayesian feature selection (Ng, Jordan)
- Augmenting Naive Bayes for Ranking (Zhang, Jiang, Su)

## Topic: Support vector machines (April 14; Winston)

Update Thu Apr 9 15:35:00 CDT 2009 -- we will be reading the paper by Drucker, Wu, and Vapnik.

Choose from one of the following 6 papers.

- Support Vector Machines for Histogram-Based Image Classification (Chapelle, Haffner, and Vapnik)
- Support Vector Machines for Spam Categorization (Drucker, Wu, and Vapnik)
- A Comparison of Methods for Multiclass Support Vector Machines (Hsu and Lin)
- Text Categorization with Support Vector Machines: Learning with Many Relevant Features (Joachims)
- Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods (Platt)
- A tutorial on support vector regression (Smola and Schölkopf)

## Topic: Unsupervised learning (April 28)

Choose from one of the following 5 papers.

- Soft vector quantization and the EM algorithm (Alpaydin)
- A Density-Based algorithm for discovering clusters in large spatial databases with noise (Ester, Kriegel, Sander, Xu)
- Large scale hierarchical clustering of protein sequences (Krause, Stoye, and Vingron)
- On spectral clustering: Analysis and an algorithm (Ng, Jordan, and Weiss)
- Combining multiple weak clusterings (Topchy, Jain, and Punch)

## Topic: Boosting (April 30)

Choose from one of the following 4 papers.

- The Application of AdaBoost for Distributed, Scalable and On-line Learning (Fan, Stolfo, and Zhang)
- Boosted Wrapper Induction (Freitag and Kushmerick)
- Generalized Multiclass AdaBoost and Its Applications to Multimedia Classification (Hao and Luo)
- Face Recognition Using Boosted Local Features (Jones and Viola)

Copyright © 2008 Greg Hamerly.

Computer Science Department

Baylor University