Search the inter-webs.
SNAP is a set of networks hosted by a prof at Stanford. Several real world examples in a variety of settings.
Net Wiki is hosted by a UNC math prof., again several links to real datasets as well as links to other data resources.
OpenFlights Has airports and routes between them (spatial network).
OpenStreetMap user edited network ...
Bob Warfield wrote a blog post (A Picture of the MultiCore Crisis) that has the kind of graph you're looking for.
It sounds like the Stanford CPU DB has what you're looking for. You can browse visualizations and download the raw data.
Your best bet is to go with major open source projects (Apache, Open Office, Mozilla, MySql, perhaps Linux). Since these are done by large online communities all communication occurs online through bug databases and forums or mailing lists (which are usually archived). If they do code reviews they may be archived with a tool like Review Board or will be ...
See Sutter, The Free Lunch is Over (2005): it has graphs, discusses how various processor characteristics relate to performance of actual programs, and makes some predictions that turned out true, as far as I can tell.
In general, you can look at hard combinatorial instances. If 50 variables is not a hard limit, try e.g., an instance arising from combinatorial block design having 105 variables described in . The actual instance in DIMACS format is here provided by the authors. For the instance, the authors write in  that: "We issue a challenge for any solver to solve ...
DBpedia provides structured information from Wikipedia.
It uses RDF, and is one of the most known Linked Data datasets.
Also of interest (but not using information from Wikipedia) may be Freebase and OpenCyc.
See the international SAT solver competition. It's a competition that tests various solvers by running them on a library of hard SAT instances. You can download the instances they used to evaluate the SAT solvers. I would suggest you look at these competitions and see if any of the instances they've used would meet your needs.
The 5th edition of Computer Architecture, A Quantitative Approach has several related graphs.
The clock rate graphs is divided in 3 parts, from a 5MHz Vax in 78 to a Sun SPARC at 16 MHz in 86 (15% per year), to an Intel Pentium 4 Xeon in 2003 at 3200 MHz(40% per year) to an Intel Nehalem Xeon in 2010 at 3330 MHz (1% per year).
The performance graph is also ...
Question: Where can I access automatically generated (spam) webpages?
You could use:
a parody generator program.
They're usually based on Markov chains (e.g. SubredditSimulator) or context-free grammars (e.g. the well known SCIGen... according to the authors "with Markov chains you might get something syntactically correct, but it is likely to be boring"!)...
Probably the most common way to represent audio for speech recognition is using the Mel-frequency cepstrum coefficients. If you're interested in finding out more about state of the art neural network based systems for speech recognition, I'd recommend checking out some of the recent work by George Dahl.
In regard to the other, more general portion of your ...
A standard approach is to pick a standard image bank of other images that you might run into that aren't apples, and use that as your other class. The way you figure out what kind of images you need is: figure out what kind of images your classifier might be run on, and try to make the training set as similar as possible to the kinds of images that it will ...
The MATLAB files for the book are accessible here.
I'm not familiar with the text, but there are .mat files in the zip named
which probably contain the data. There are also a handful of TIFF files (images) from which he draws the other data.
There are a couple of test sets mentioned in Stochastic blockmodels and community structure in networks by Karrer and Newman. One of them is the Karate Club Network, which is rather small, and the other is the Political Blog Network which is bigger. As far as I know, there is no gold-standard for testing clustering algorithms.
I've been visiting all the links provided by Nick. They do look wonderful indeed and I have added all those sites to my bookmarks. Hope that the following link especially designed to test search algorithms suits your needs as well:
Pathfinding Benchmarks by Nathan Sturtevant. It contains various maps from different video games and also other artificial ...
I propose this, more direct to code (in java, c, etc.)
read each line (until CR/LF)
at begining, read a number, until spaces => number n
after that, take next characters of line
split them by ,
for each, trim (delete space before or after) => strings s1, s2, s3
then output n s1 CRLF n s2 CRLF n s3, etc.
The short answer is: I don't know.
There's been lots of work on visualizing the MNIST data set, in 2 dimensions, and even in 3 dimensions.
For instance, here's an embedding into 2D space using a neighbor embedding visualization technique (a highly nonlinear embedding):
Credit: Zhirong Yang, Jaakko Peltonen, and Samuel Kaski. See their ...
You have a dataset with at least billions of entries, whose size is that of a medium-sized hard disk. This is large. If something takes only “many hours” and not many months, that's as good as you can expect.
Sorting the file is a viable strategy to get rid of the duplicates. You'll also need to canonicalize the pairs. If you stick with a flat file, the ...
You are probably interested in IAPS, and want to read Analysis of Physiological Signals for Emotion Recognition Based on Support Vector Machine.
But to be honest this classification is doomed in advance, not only pictures are stripped of below threshold of effector activisation but also you do not get baseline as point of reference.
If you are really ...
You can group attributes together, but (assuming you're building some kind of decision tree) your decision tree will no longer be a binary tree, and you probably won't be any better off than just splitting by one attribute. Say for example you group $x_1$ and $x_2$ together, then there are 4 possible values for this new meta-attribute. You can use the same ...
If you want to detect anomalies, my suggestion would be to build a classifier to classify individual frames of the video to determine whether that frame contains an anomaly. You'll need to categorize what you count as an "anomaly" and arrange for labelled data in your training set. But then you can extract all 6800 frames from the training videos, label ...
Here is a fairly old link, which only lists a few classes specifically, but it might be a place to start.
I'd agree with David Eppstein that a longer list might not be all that useful. There's no quick way to tell for sure whether a given CA rule is Class 4, except in the really simple cases. In many cases, with enough work you may be able to design ...
Let's start with comparing two time series. To compare them, you probably want to use cross-correlation. The cross-correlation computes the correlation between signal $f$ and a delayed version of signal $g$, for all possible delays. An efficient way to compute the cross-correlation is indeed by using a FFT, taking the product, and then applying an inverse ...
Metadata is an infamously ill-defined term. Roughly speaking, it's trying to get at a distinction along the following lines: data is the "content" you care about; metadata is other information that is secondary or exists primarily for technical reasons (e.g., addressing information, tags, technical data to facilitate storage or communication).
A classical ...
You can for instance take as ground truth data the movielens dataset, remove some rating links between users and movies. You can rank your algorithm by counting the number of link that you can guess right. Usually machine learning algorithm also guess the rating score.
In this sort of situation, two standard answers are:
Figure out what the practical applications of your algorithm are. Find a dataset associated with that particular application, and try your algorithm on it and see how well it works. Measure success using some metric that is appropriate for that particular application.
Look through the research ...
Another option is to use an encoding scheme for coordinates like geohash (http://en.wikipedia.org/wiki/Geohash).
From wikipedia: The main usages of Geohashes are as a unique identifier.
represent point data e.g. in databases. Geohashes have also been proposed to be used for geotagging. When used in a database, the structure of geohashed data has two ...
For quick look-up on multidimensional points you need to create a R-tree index (http://en.wikipedia.org/wiki/R-tree) or any of its variants (http://en.wikipedia.org/wiki/Spatial_index#Spatial_index).
From wikipedia: R-trees are tree data structures used for spatial access methods, i.e., for indexing multi-dimensional information such as geographical ...
Hash map / Hash table (depending on the language used)
Per comments, Some reasoning why:
According to Introduction to Algorithms by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, the big O of hash tables is O(1) to O(N) for insert, size, and time.
So best case is O(1) solution. If you are able to control the upper and lower ...