1. User Knowledge Modeling: It is the real dataset about the students' knowledge status about the subject of Electrical DC Machines. The dataset had been obtained from Ph.D. Thesis.
2. Computer Hardware: Relative CPU Performance Data, described in terms of its cycle time, memory size, etc.
3. Letter Recognition: Database of character image features; try to identify the letter
4. Optical Recognition of Handwritten Digits: Two versions of this database available; see folder
5. Pen-Based Recognition of Handwritten Digits: Digit database of 250 samples from 44 writers
6. Gisette: GISETTE is a handwritten digit recognition problem. The problem is to separate the highly confusible digits '4' and '9'. This dataset is one of five datasets of the NIPS 2003 feature selection challenge.
7. Semeion Handwritten Digit: 1593 handwritten digits from around 80 persons were scanned, stretched in a rectangular box 16x16 in a gray scale of 256 values.
8. YouTube Multiview Video Games Dataset: This dataset contains about 120k instances, each described by 13 feature types, with class information, specially useful for exploring multiview topics (cotraining, ensembles, clustering,..).
9. Multiple Features: This dataset consists of features of handwritten numerals (`0'--`9') extracted from a collection of Dutch utility maps
10. Page Blocks Classification: The problem consists of classifying all the blocks of the page layout of a document that has been detected by a segmentation process.
11. Spambase: Classifying Email as Spam or Non-Spam
12. URL Reputation: Anonymized 120-day subset of the ICML-09 URL data containing 2.4 million examples and 3.2 million features.
13. Energy efficiency: This study looked into assessing the heating load and cooling load requirements of buildings (that is, energy efficiency) as a function of building parameters.
14. Daily and Sports Activities: The dataset comprises motion sensor data of 19 daily and sports activities each performed by 8 subjects in their own style for 5 minutes. Five Xsens MTx units are used on the torso, arms, and legs.
15. banknote authentication: Data were extracted from images that were taken for the evaluation of an authentication procedure for banknotes.
16. Gas Sensor Array Drift Dataset at Different Concentrations: This archive contains 13910 measurements from 16 chemical sensors exposed to 6 different gases at various concentration levels.
17. ISOLET: Goal: Predict which letter-name was spoken--a simple classification task.
18. SECOM: Data from a semi-conductor manufacturing process
19. Concrete Slump Test: Concrete is a highly complex material. The slump flow of concrete is not only determined by the water content, but that is also influenced by other concrete ingredients.
20. Wall-Following Robot Navigation Data: The data were collected as the SCITOS G5 robot navigates through the room following the wall in a clockwise direction, for 4 rounds, using 24 ultrasound sensors arranged circularly around its 'waist'.
21. PEMS-SF: 15 months worth of daily data (440 daily records) that describes the occupancy rate, between 0 and 1, of different car lanes of the San Francisco bay area freeways across time.
22. Reuter_50_50: The dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition.
23. Gas Sensor Array Drift Dataset: This archive contains 13910 measurements from 16 chemical sensors utilized in simulations for drift compensation in a discrimination task of 6 gases at various levels of concentrations.
24. OPPORTUNITY Activity Recognition: The OPPORTUNITY Dataset for Human Activity Recognition from Wearable, Object, and Ambient Sensors is a dataset devised to benchmark human activity recognition algorithms (classification, automatic data segmentation, sensor fusion, feature extraction, etc).
25. PAMAP2 Physical Activity Monitoring: The PAMAP2 Physical Activity Monitoring dataset contains data of 18 different physical activities, performed by 9 subjects wearing 3 inertial measurement units and a heart rate monitor.
26. First-order theorem proving: Given a theorem, predict which of five heuristics will give the fastest proof when used by a first-order prover. A sixth prediction declines to attempt a proof, should the theorem be too difficult.