I have a problem related to graph.
I am not a computer science grad hence needed a some quick intro on what is graph and were can i read about graph and how to solve graph related problem in c++ or in general.
The boost graph library may be a starting point and give you some code for solving your graph related problems.
Please see Graph problems in the Stony Brook Algorithm Repository,
and a cute lecture by Xavier Llora.
I would start by studying a few specific algorithms. Dijkstra's algorithm and the graph closure algorithm are good places to start. Also most introductory computer science (eg. Data Structures) texts have a section on graphs. I used this book, mostly after I was already pretty comfortable with most of the material though. It takes a pretty formal approach, so if your maths is strong you might like it.
The community might be able to give you better pointers if you mentioned something specific that you're trying to solve (if there is such a thing).
This is a very cool tool for representing graphs
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can someone point me to some methods for perspective ray casting. I have seen ray splitting techniques. What other casting methods are available? Which one is easiest to implement?
Follow this Ray-Casting Tutorial For Game Development And Other Purposes by F. Permadi. It describes the easiest method. It was also described in the Tricks of the Game-Programming Gurus book by André LaMothe.
I've implemented it several times. Takes ~1K lines of code or less (depending on your programming language and how advanced you want your raycaster to be). Basic school math.
Just Curious. I'm currently foraying into the world of Java coding, and, was wondering weather Geometry can come useful in the kind of programming a Beginner to Intermediate Skill level Java coder has.
Depends on what you will code. Java is just a language laden with tons of useful apis. But knowing a bit of geometry can never hurt. If you are gonna code a lot of layouts you might find knowledge of shapes and forms can come pretty handy.
Even without real application needs, some exercises on geometry or other forms of maths can only help you design/code better.
It can be useful if the problems you need to solve require an application of geometry to do so. Other than that, I think that the only benefit geometry would provide would be the added benefit of thinking critically.
Depends what you mean by "basic". In these days of graphical user interfaces, nearly everyone needs Pythagoras sooner or later...
You can learn Java without knowing a single point of geometry.
That being said, I can't think of many situations where knowing geometry would be a bad thing.
Is there a research paper/book that I can read which can tell me for the problem at hand what sort of feature selection algorithm would work best.
I am trying to simply identify twitter messages as pos/neg (to begin with). I started out with Frequency based feature selection (having started with NLTK book) but soon realised that for a similar problem various individuals have choosen different algorithms
Although I can try Frequency based, mutual information, information gain and various other algorithms the list seems endless.. and was wondering if there an efficient way then trial and error.
any advice
Have you tried the book I recommended upon your last question? It's freely available online and entirely about the task you are dealing with: Sentiment Analysis and Opinion Mining by Pang and Lee. Chapter 4 ("Extraction and Classification") is just what you need!
I did an NLP course last term, and it came pretty clear that sentiment analysis is something that nobody really knows how to do well (yet). Doing this with unsupervised learning is of course even harder.
There's quite a lot of research going on regarding this, some of it commercial and thus not open to the public. I can't point you to any research papers but the book we used for the course was this (google books preview). That said, the book covers a lot of material and might not be the quickest way to find a solution to this particular problem.
The only other thing I can point you towards is to try googling around, maybe in scholar.google.com for "sentiment analysis" or "opinion mining".
Have a look at the NLTK movie_reviews corpus. The reviews are already pos/neg categorized and might help you with training your classifier. Although the language you find in Twitter is probably very different from those.
As a last note, please post any successes (or failures for that matter) here. This issue will come up later for sure at some point.
Unfortunately, there is no silver bullet for anything when dealing with machine learning. It's usually referred to as the "No Free Lunch" theorem. Basically a number of algorithms work for a problem, and some do better on some problems and worse on others. Over all, they all perform about the same. The same feature set may cause one algorithm to perform better and another to perform worse for a given data set. For a different data set, the situation could be completely reversed.
Usually what I do is pick a few feature selection algorithms that have worked for others on similar tasks and then start with those. If the performance I get using my favorite classifiers is acceptable, scrounging for another half percentage point probably isn't worth my time. But if it's not acceptable, then it's time to re-evaluate my approach, or to look for more feature selection methods.
I've noticed that a number of top universities are offering courses where students are taught subjects relating to Computer Graphics for their CS majors. Sadly this is something not offered by my university and something I would really like to get into sometime in the next couple of years.
A couple of the projects I've found from some universities are great, although I'm mostly interested in two things:
Raytracing:
I want to write a Raytracer within the next two years. What do I need to know? I'm not a fantastic programmer yet (Java, C and Prolog are my main languages as of today) but I'm slowly learning every day. Also, my Math background isn't all that great, so any pointers on books to read or advice on writing such a program would be fantastic. I tend to pick these things up pretty quickly so feel free to chuck references at me.
Programming 3D Rendered Models
I've looked at a couple of projects where students have developed models and used them in games. I've made a couple of 2D games with raster images but have never worked with 3D models. What would I need to learn in regards to programming these models? If it helps I used to be okay with 3D Studio Max and Cinema4D (although every single course seems to use Maya), but haven't touched it in about four years.
Sorry for posting such vague and, let's be honest, stupid questions. It's just something I've wanted to do for a while and something that'd be good as a large project for me to develop in my own time.
Related Questions
Literature and Tutorials for Writing a Ray Tracer
I can recommend pbrt, it's a book and a physically-based renderer used to teach computer science graduates. The description of the maths used is nice and clear, and since it is written in the 'literate programming' you can see the appropriate code (in C++) too.
The book "Computer Graphics: Principles and Practice" (known in the Computer Graphics circles as the "Foley-VanDam") is the basic for most computer graphics courses, and it covers the topic of implementing a ray-tracer in much detail. It is quite dated, but it's still the best, afaik, and the basic principles remain the same.
I also second the recommendation for Eric Lengyel's Mathematics for 3D Game Programming and Computer Graphics. It's not as thorough, but it's a wonderful review of the math basics you need for 3D programming, it has very useful summaries at the end of each chapter, and it's written in an approachable, not too scary way.
In addition, you'll probably want some OpenGL or DirectX basics. It's easier to start working with a 3D API, then learn the underlying maths than the opposite (in my opinion), but both options are possible. Just look for OpenGL on SO and you should find a couple of good references as well.
The 2000 ICFP Programming Contest asked participants to build a ray tracer in three days. They have a good specification for a simple ray tracer, and you can get code for the winning entries and some other entries as well. There were entries in a large number of different programming languages. This might be a nice way for you to get started.
The briefest useful answer I can give is that most of the important algorithms can be found in Real-Time Rendering by Tomas Akenine-Möller, Eric Haines, and Naty Hoffman, and the bibliography at the end has references to the necessary maths. Their website has a recommended reading list as well.
The most useful math book I've read on the subject is Eric Lengyel's Mathematics for 3D Game Programming and Computer Graphics. The maths you need most are geometry (obviously) and linear algebra (for dealing with all the matrices).
I took such a class last year, and I believe that the class was wonderful for forcing students to learn the math behind the computer graphics - not just the commands for making a computer do what you want.
My professor has a site located here and it has his lecture notes and problem sets that you can take a look through.
Our final project was indeed a raytracer, but once you know the mathematics behind it, coding (an inefficient one) is trivial.
For a mathematical introduction into these topics, see
http://graphics.idav.ucdavis.edu/education/GraphicsNotes/homepage.html
Check http://www.scratchapixel.com/lessons/3d-basic-lessons/lesson-1-writing-a-simple-raytracer/
This is a very good place to learn about ray tracing and rendering in general.
Does anyone known of a a good reference for canonical CS problems?
I'm thinking of things like "the sorting problem", "the bin packing problem", "the travailing salesman problem" and what not.
edit: websites preferred
You can probably find the best in an algorithms textbook like Introduction to Algorithms. Though I've never read that particular book, it's quite renowned for being thorough and would probably contain most of the problems you're likely to encounter.
"Computers and Intractability: A guide to the theory of NP-Completeness" by Garey and Johnson is a great reference for this sort of thing, although the "solved" problems (in P) are obviously not given much attention in the book.
I'm not aware of any good on-line resources, but Karp's seminal paper Reducibility among Combinatorial Problems (1972) on reductions and complexity is probably the "canonical" reference for Hard Problems.
Have you looked at Wikipedia's Category:Computational problems and Category:NP Complete Problems pages? It's probably not complete, but they look like good starting points. Wikipedia seems to do pretty well in CS topics.
I don't think you'll find the answers to all those problems in only one book. I've never seen any decent, comprehensive website on algorithms, so I'd recommend you to stick to the books. That said, you can always get some introductory material on canonical algorithm texts (there are always three I usually recommend: CLRS, Manber, Aho, Hopcroft and Ullman (this one is a bit out of date in some key topics, but it's so formal and well-written that it's a must-read). All of them contain important combinatorial problems that are, in some sense, canonical problems in computer science. After learning some fundamentals in graph theory you'll be able to move to Network Flows and Linear Programming. These comprise a set of techniques that will ultimately solve most problems you'll encounter (linear programming with the variables restricted to integer values is NP-hard). Network flows deals with problems defined on graphs (with weighted/capacitated edges) with very interesting applications in fields that seemingly have no relationship to graph theory whatsoever. THE textbook on this is Ahuja, Magnanti and Orlin's. Linear programming is some kind of superset of network flows, and deals with optimizing a linear function on variables subject to restrictions in the form of a linear system of equations. A book that emphasizes the relationship to network flows is Bazaraa's. Then you can move on to integer programming, a very valuable tool that presents many natural techniques for modelling problems like bin packing, task scheduling, the knapsack problem, and so on. A good reference would be L. Wolsey's book.
You definitely want to look at NIST's Dictionary of Algorithms and Data Structures. It's got the traveling salesman problem, the Byzantine generals problem, the dining philosophers' problem, the knapsack problem (= your "bin packing problem", I think), the cutting stock problem, the eight queens problem, the knight's tour problem, the busy beaver problem, the halting problem, etc. etc.
It doesn't have the firing squad synchronization problem (I'm surprised about that omission) or the Jeep problem (more logistics than computer science).
Interestingly enough there's a blog on codinghorror.com which talks about some of these in puzzle form. (I can't remember whether I've read Smullyan's book cited in the blog, but he is a good compiler of puzzles & philosophical musings. Martin Gardner and Douglas Hofstadter and H.E. Dudeney are others.)
Also maybe check out the Stony Brook Algorithm Repository.
(Or look up "combinatorial problems" on google, or search for "problem" in Wolfram Mathworld or look at Hilbert's problems, but in all these links many of them are more pure-mathematics than computer science.)
#rcreswick those sound like good references but fall a bit shy of what I'm thinking of. (However, for all I know, it's the best there is)
I'm going to not mark anything as accepted in hopes people might find a better reference.
Meanwhile, I'm going to list a few problems here, fell free to add more
The sorting problem Find an order for a set that is monotonic in a given way
The bin packing problem partition a set into a minimum number of sets where each subset is "smaller" than some limit
The travailing salesman problem Find a Hamiltonian cycle in a weighted graph with the minimum total weight