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  • ANN for decompiler?

    - by Rhythmic Algorithm
    Has there ever been any attempts at utilizing artificial neural networks in decompilation? It would be nice if it was possible to provide the trimmed semantics of source along with the code in to a neural network so it could learn the connection between the two. I assume this would likely lose it's effectiveness when there is optimizations and maybe work better for high level languages too but I'm interested in hearing any attempts anyone has had at this.

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  • 'Similarity' in Data Mining

    - by Shailesh Tainwala
    In the field of Data Mining, is there a specific sub-discipline called 'Similarity'? If yes, what does it deal with. Any examples, links, references will be helpful. Also, being new to the field, I would like the community opinion on how closely related Data Mining and Artificial Intelligence are. Are they synonyms, is one the subset of the other? Thanks in advance for sharing your knowledge.

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  • Applications of Unification?

    - by Ravi
    What are (practical) applications of Unification ? Where it is been used in real world? I couldn't get the whole idea of what it is really about and why its considered as a part of Artificial Intelligence.

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  • AIMA in disgrace?

    - by lmsasu
    Hi, according to some reviews on Amazon, the AIMA 3rd Edition is quite a disappointment... minor update not worth the money. In your opinion, which is then a more suitable introductory textbook on artificial intelligence?

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  • Intelligent "Subtraction" of one text logfile from another

    - by Vi
    Example: Application generates large text log file A with many different messages. It generates similarly large log file B when does not function correctly. I want to see what messages in file B are essentially new, i.e. to filter-out everything from A. Trivial prototype is: Sort | uniq both files Join files sort | uniq -c grep -v "^2" This produces symmetric difference and inconvenient. How to do it better? (including non-symmetric difference and preserving of messages order in B) Program should first analyse A and learn which messages are common, then analyse B showing with messages needs attention. Ideally it should automatically disregard things like timestamps, line numbers or other volatile things. Example. A: 0:00:00.234 Received buffer 0x324234 0:00:00.237 Processeed buffer 0x324234 0:00:00.238 Send buffer 0x324255 0:00:03.334 Received buffer 0x324255 0:00:03.337 Processeed buffer 0x324255 0:00:03.339 Send buffer 0x324255 0:00:05.171 Received buffer 0x32421A 0:00:05.173 Processeed buffer 0x32421A 0:00:05.178 Send buffer 0x32421A B: 0:00:00.134 Received buffer 0x324111 0:00:00.137 Processeed buffer 0x324111 0:00:00.138 Send buffer 0x324111 0:00:03.334 Received buffer 0x324222 0:00:03.337 Processeed buffer 0x324222 0:00:03.338 Error processing buffer 0x324222 0:00:03.339 Send buffer 0x3242222 0:00:05.271 Received buffer 0x3242FA 0:00:05.273 Processeed buffer 0x3242FA 0:00:05.278 Send buffer 0x3242FA 0:00:07.280 Send buffer 0x3242FA failed Result: 0:00:03.338 Error processing buffer 0x324222 0:00:07.280 Send buffer 0x3242FA failed One of ways of solving it can be something like that: Split each line to logical units: 0:00:00.134 Received buffer 0x324111,0:00:00.134,Received,buffer,0x324111,324111,Received buffer, \d:\d\d:\d\d\.\d\d\d, \d+:\d+:\d+.\d+, 0x[0-9A-F]{6}, ... It should find individual words, simple patterns in numbers, common layouts (e.g. "some date than text than number than text than end_of_line"), also handle combinations of above. As it is not easy task, user assistance (adding regexes with explicit "disregard that","make the main factor","don't split to parts","consider as date/number","take care of order/quantity of such messages" rules) should be supported (but not required) for it. Find recurring units and "categorize" lines, filter out too volatile things like timestamps, addresses or line numbers. Analyse the second file, find things that has new logical units (one-time or recurring), or anything that will "amaze" the system which has got used to the first file. Example of doing some bit of this manually: $ cat A | head -n 1 0:00:00.234 Received buffer 0x324234 $ cat A | egrep -v "Received buffer" | head -n 1 0:00:00.237 Processeed buffer 0x324234 $ cat A | egrep -v "Received buffer|Processeed buffer" | head -n 1 0:00:00.238 Send buffer 0x324255 $ cat A | egrep -v "Received buffer|Processeed buffer|Send buffer" | head -n 1 $ cat B | egrep -v "Received buffer|Processeed buffer|Send buffer" 0:00:03.338 Error processing buffer 0x324222 0:00:07.280 Send buffer 0x3242FA failed This is a boring thing (there are a lot of message types); also I can accidentally include some too broad pattern. Also it can't handle complicated things like interrelation between messages. I know that it is AI-related. May be there are already developed tools?

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  • Porting a piece of Lisp code to Clojure (PAIP)

    - by Robert Brown
    I'm reading Paradigms of Artificial Intelligence Programming (PAIP) by Peter Norvig and I'm trying to write all the code in Clojure rather than common Lisp. However I'm stuck on this piece of code on page 39: (defparameter *simple-grammar* '((sentence -> (noun-phrase verb-phrase)) (noun-phrase -> (Article Noun)) (verb-phrase -> (Verb noun-phrase)) (Article -> the a) (Noun -> man ball woman table) (Verb -> hit took saw liked)) "A grammar for a trivial subset of English.") (defvar *grammar* *simple-grammar*) How can I translate this into Clojure? Thanks.

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  • Predict Stock Market Values

    - by mrlinx
    I'm building a web semantic project that gathers the maximum ammount of historic data about a certain company and tries to predict its future market stock values. For data I have the historic stock values (not normalized), news (0 to 1 polarity) and subjective content (also a 0 to 1 polarity). What is the best AI system to train and use for this kind of objective? Is a simple NN with back-propagation training the best I can hope for? update: Everyone is concerned about the quality of this system. Although I'm pretty sure the system is as good as a random prediction (or even worse), this is a school project around artificial intelligence and web semantics. Therefore I'm only concerned in picking the best kind of train method for the data I have (NN, RBF, SVM, Bayes, neuro-fuzzy, etc). Its not about making money.

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  • Anyone has implemented SMA* search algorithm?

    - by Endy
    I find the algorithm description in AIMA (Artificial Intelligence: A Modern Approach) is not correct at all. What does 'necessary' mean? What is the memory limit? The queue size or processed nodes? What if the current node has no children at all? I am wondering if this algorithm itself is correct or not. Because I searched the Internet and nobody has implemented it yet. Thanks.

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  • Genetics algorithms theoretical question

    - by mandelart
    Hi All! I'm currently reading "Artificial Intelligence: A Modern Approach" (Russell+Norvig) and "Machine Learning" (Mitchell) - and trying to learn basics of AINN. In order to understand few basic things I have two 'greenhorn' questions: Q1: In a genetic algorithm given the two parents A and B with the chromosomes 001110 and 101101, respectively, which of the following offspring could have resulted from a one-point crossover? a: 001101 b: 001110 Q2: Which of the above offspring could have resulted from a two-point crossover? and why? Please advise.

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  • Connect 4 with neural network: evaluation of draft + further steps

    - by user89818
    I would like to build a Connect 4 engine which works using an artificial neural network - just because I'm fascinated by ANNs. I'be created the following draft of the ANN structure. Would it work? And are these connections right (even the cross ones)? Could you help me to draft up an UML class diagram for this ANN? I want to give the board representation to the ANN as its input. And the output should be the move to chose. The learning should later be done using backpropagation and the sigmoid function should be applied. The engine will play against human players. And depending on the result of the game, the weights should be adjusted then.

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  • Neural Network Inputs and Outputs to meaningful values

    - by Micheal
    I'm trying to determine how to transform my "meaningful input" into data for an Artificial Neural Network and how to turn the output into "meaningful output". The way I can always see of doing it is by convering everything to categories with binary values. For example, rather than outputting age, having a 0-1 for <10, a 0-1 for 10 - 19, etc. Same with the inputs, where I might be using for example, hair colour. Is the only way to turn this into input to have Blonde 0-1, Brown 0-1, etc? Am I missing some entire topic of ANNs? Most of the books and similar I read use theoretical examples.

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  • Creating a smart text generator

    - by royrules22
    I'm doing this for fun (or as 4chan says "for teh lolz") and if I learn something on the way all the better. I took an AI course almost 2 years ago now and I really enjoyed it but I managed to forget everything so this is a way to refresh that. Anyway I want to be able to generate text given a set of inputs. Basically this will read forum inputs (or maybe Twitter tweets) and then generate a comment based on the learning. Now the simplest way would be to use a Markov Chain Text Generator but I want something a little bit more complex than that as the MKC basically only learns by word order (which word is more likely to appear after word x given the input text). I'm trying to see if there's something I can do to make it a little bit more smarter. For example I want it to do something like this: Learn from a large selection of posts in a message board but don't weight it too much For each post: Learn from the other comments in that post and weigh these inputs higher Generate comment and post See what other users' reaction to your post was. If good weigh it positively so you make more posts that are similar to the one made, and vice versa if negative. It's the weighing and learning from mistakes part that I'm not sure how to implement. I thought about Artificial Neural Networks (mainly because I remember enjoying that chapter) but as far as I can tell that's mainly used to classify things (i.e. given a finite set of choices [x1...xn] which x is this given input) not really generate anything. I'm not even sure if this is possible or if it is what should I go about learning/figuring out. What algorithm is best suited for this? To those worried that I will use this as a bot to spam or provide bad answers to SO, I promise that I will not use this to provide (bad) advice or to spam for profit. I definitely will not post it's nonsensical thoughts on SO. I plan to use it for my own amusement. Thanks!

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  • Neural Networks or Human-computer interaction

    - by Shahin
    I will be entering my third year of university in my next academic year, once I've finished my placement year as a web developer, and I would like to hear some opinions on the two modules in the Title. I'm interested in both, however I want to pick one that will be relevant to my career and that I can apply to systems I develop. I'm doing an Internet Computing degree, it covers web development, networking, database work and programming. Though I have had myself set on becoming a web developer I'm not so sure about that any more so am trying not to limit myself to that area of development. I know HCI would help me as a web developer, but do you think it's worth it? Do you think Neural Network knowledge could help me realistically in a system I write in the future? Thanks. EDIT: Hi guys, I thought it would be useful to follow-up with what I decided to do and how it's worked out. I picked Artificial Neural Networks over HCI, and I've really enjoyed it. Having a peek into cognitive science and machine learning has ignited my interest for the subject area, and I will be hoping to take on a postgraduate project a few years from now when I can afford it. I have got a job which I am starting after my final exams (which are in a few days) and I was indeed asked if I had done a module in HCI or similar. It didn't seem to matter, as it isn't a front-end developer position! I would recommend taking the module if you have it as an option, as well as any module consisting of biological computation, it will open up more doors should you want to go onto postgraduate research in the future. Thanks again, Shahin

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  • Constraint Satisfaction Problem

    - by Carl Smotricz
    I'm struggling my way through Artificial Intelligence: A Modern Approach in order to alleviate my natural stupidity. In trying to solve some of the exercises, I've come up against the "Who Owns the Zebra" problem, Exercise 5.13 in Chapter 5. This has been a topic here on SO but the responses mostly addressed the question "how would you solve this if you had a free choice of problem solving software available?" I accept that Prolog is a very appropriate programming language for this kind of problem, and there are some fine packages available, e.g. in Python as shown by the top-ranked answer and also standalone. Alas, none of this is helping me "tough it out" in a way as outlined by the book. The book appears to suggest building a set of dual or perhaps global constraints, and then implementing some of the algorithms mentioned to find a solution. I'm having a lot of trouble coming up with a set of constraints suitable for modelling the problem. I'm studying this on my own so I don't have access to a professor or TA to get me over the hump - this is where I'm asking for your help. I see little similarity to the examples in the chapter. I was eager to build dual constraints and started out by creating (the logical equivalent of) 25 variables: nationality1, nationality2, nationality3, ... nationality5, pet1, pet2, pet3, ... pet5, drink1 ... drink5 and so on, where the number was indicative of the house's position. This is fine for building the unary constraints, e.g. The Norwegian lives in the first house: nationality1 = { :norway }. But most of the constraints are a combination of two such variables through a common house number, e.g. The Swede has a dog: nationality[n] = { :sweden } AND pet[n] = { :dog } where n can range from 1 to 5, obviously. Or stated another way: nationality1 = { :sweden } AND pet1 = { :dog } XOR nationality2 = { :sweden } AND pet2 = { :dog } XOR nationality3 = { :sweden } AND pet3 = { :dog } XOR nationality4 = { :sweden } AND pet4 = { :dog } XOR nationality5 = { :sweden } AND pet5 = { :dog } ...which has a decidedly different feel to it than the "list of tuples" advocated by the book: ( X1, X2, X3 = { val1, val2, val3 }, { val4, val5, val6 }, ... ) I'm not looking for a solution per se; I'm looking for a start on how to model this problem in a way that's compatible with the book's approach. Any help appreciated.

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  • Graph Tour with Uniform Cost Search in Java

    - by user324817
    Hi. I'm new to this site, so hopefully you guys don't mind helping a nub. Anyway, I've been asked to write code to find the shortest cost of a graph tour on a particular graph, whose details are read in from file. The graph is shown below: http://img339.imageshack.us/img339/8907/graphr.jpg This is for an Artificial Intelligence class, so I'm expected to use a decent enough search method (brute force has been allowed, but not for full marks). I've been reading, and I think that what I'm looking for is an A* search with constant heuristic value, which I believe is a uniform cost search. I'm having trouble wrapping my head around how to apply this in Java. Basically, here's what I have: Vertex class - ArrayList<Edge> adjacencies; String name; int costToThis; Edge class - final Vertex target; public final int weight; Now at the moment, I'm struggling to work out how to apply the uniform cost notion to my desired goal path. Basically I have to start on a particular node, visit all other nodes, and end on that same node, with the lowest cost. As I understand it, I could use a PriorityQueue to store all of my travelled paths, but I can't wrap my head around how I show the goal state as the starting node with all other nodes visited. Here's what I have so far, which is pretty far off the mark: public static void visitNode(Vertex vertex) { ArrayList<Edge> firstEdges = vertex.getAdjacencies(); for(Edge e : firstEdges) { e.target.costToThis = e.weight + vertex.costToThis; queue.add(e.target); } Vertex next = queue.remove(); visitNode(next); } Initially this takes the starting node, then recursively visits the first node in the PriorityQueue (the path with the next lowest cost). My problem is basically, how do I stop my program from following a path specified in the queue if that path is at the goal state? The queue currently stores Vertex objects, but in my mind this isn't going to work as I can't store whether other vertices have been visited inside a Vertex object. Help is much appreciated! Josh

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  • Gomoku array-based AI-algorithm?

    - by Lasse V. Karlsen
    Way way back (think 20+ years) I encountered a Gomoku game source code in a magazine that I typed in for my computer and had a lot of fun with. The game was difficult to win against, but the core algorithm for the computer AI was really simply and didn't account for a lot of code. I wonder if anyone knows this algorithm and has some links to some source or theory about it. The things I remember was that it basically allocated an array that covered the entire board. Then, whenever I, or it, placed a piece, it would add a number of weights to all locations on the board that the piece would possibly impact. For instance (note that the weights are definitely wrong as I don't remember those): 1 1 1 2 2 2 3 3 3 444 1234X4321 3 3 3 2 2 2 1 1 1 Then it simply scanned the array for an open location with the lowest or highest value. Things I'm fuzzy on: Perhaps it had two arrays, one for me and one for itself and there was a min/max weighting? There might've been more to the algorithm, but at its core it was basically an array and weighted numbers Does this ring a bell with anyone at all? Anyone got anything that would help?

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  • Machine learning challenge: diagnosing program in java/groovy (datamining, machine learning)

    - by Registered User
    Hi All! I'm planning to develop program in Java which will provide diagnosis. The data set is divided into two parts one for training and the other for testing. My program should learn to classify from the training data (BTW which contain answer for 30 questions each in new column, each record in new line the last column will be diagnosis 0 or 1, in the testing part of data diagnosis column will be empty - data set contain about 1000 records) and then make predictions in testing part of data :/ I've never done anything similar so I'll appreciate any advice or information about solution to similar problem. I was thinking about Java Machine Learning Library or Java Data Mining Package but I'm not sure if it's right direction... ? and I'm still not sure how to tackle this challenge... Please advise. All the best!

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  • OCR with Neural network: data extraction

    - by Sebastian Hoitz
    I'm using the AForge library framework and its neural network. At the moment when I train my network I create lots of images (one image per letter per font) at a big size (30 pt), cut out the actual letter, scale this down to a smaller size (10x10 px) and then save it to my harddisk. I can then go and read all those images, creating my double[] arrays with data. At the moment I do this on a pixel basis. So once I have successfully trained my network I test the network and let it run on a sample image with the alphabet at different sizes (uppercase and lowercase). But the result is not really promising. I trained the network so that RunEpoch had an error of about 1.5 (so almost no error), but there are still some letters left that do not get identified correctly in my test image. Now my question is: Is this caused because I have a faulty learning method (pixelbased vs. the suggested use of receptors in this article: http://www.codeproject.com/KB/cs/neural_network_ocr.aspx - are there other methods I can use to extract the data for the network?) or can this happen because my segmentation-algorithm to extract the letters from the image to look at is bad? Does anyone have ideas on how to improve it?

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  • What is the best Battleship AI?

    - by John Gietzen
    Battleship! Back in 2003, (when I was 17,) I competed in a Battleship AI coding competition. Even though I lost that tournament, I had a lot of fun and learned a lot from it. Now, I would like to resurrect this competition, in the search of the best battleship AI. Here is the framework: Battleship.zip The winner will be awarded +450 reputation! The competition will be held starting on the 17th of November, 2009. No entries or edits later than zero-hour on the 17th will be accepted. (Central Standard Time) Submit your entries early, so you don't miss your opportunity! To keep this OBJECTIVE, please follow the spirit of the competition. Rules of the game: The game is be played on a 10x10 grid. Each competitor will place each of 5 ships (of lengths 2, 3, 3, 4, 5) on their grid. No ships may overlap, but they may be adjacent. The competitors then take turns firing single shots at their opponent. A variation on the game allows firing multiple shots per volley, one for each surviving ship. The opponent will notify the competitor if the shot sinks, hits, or misses. Game play ends when all of the ships of any one player are sunk. Rules of the competition: The spirit of the competition is to find the best Battleship algorithm. Anything that is deemed against the spirit of the competition will be grounds for disqualification. Interfering with an opponent is against the spirit of the competition. Multithreading may be used under the following restrictions: No more than one thread may be running while it is not your turn. (Though, any number of threads may be in a "Suspended" state). No thread may run at a priority other than "Normal". Given the above two restrictions, you will be guaranteed at least 3 dedicated CPU cores during your turn. A limit of 1 second of CPU time per game is allotted to each competitor on the primary thread. Running out of time results in losing the current game. Any unhandled exception will result in losing the current game. Network access and disk access is allowed, but you may find the time restrictions fairly prohibitive. However, a few set-up and tear-down methods have been added to alleviate the time strain. Code should be posted on stack overflow as an answer, or, if too large, linked. Max total size (un-compressed) of an entry is 1 MB. Officially, .Net 2.0 / 3.5 is the only framework requirement. Your entry must implement the IBattleshipOpponent interface. Scoring: Best 51 games out of 101 games is the winner of a match. All competitors will play matched against each other, round-robin style. The best half of the competitors will then play a double-elimination tournament to determine the winner. (Smallest power of two that is greater than or equal to half, actually.) I will be using the TournamentApi framework for the tournament. The results will be posted here. If you submit more than one entry, only your best-scoring entry is eligible for the double-elim. Good luck! Have fun! EDIT 1: Thanks to Freed, who has found an error in the Ship.IsValid function. It has been fixed. Please download the updated version of the framework. EDIT 2: Since there has been significant interest in persisting stats to disk and such, I have added a few non-timed set-up and tear-down events that should provide the required functionality. This is a semi-breaking change. That is to say: the interface has been modified to add functions, but no body is required for them. Please download the updated version of the framework. EDIT 3: Bug Fix 1: GameWon and GameLost were only getting called in the case of a time out. Bug Fix 2: If an engine was timing out every game, the competition would never end. Please download the updated version of the framework. EDIT 4: Results!

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  • Minimax algorithm: Cost/evaluation function?

    - by Dave
    Hi guys, A school project has me writing a Date game in C++ (example at http://www.cut-the-knot.org/Curriculum/Games/Date.shtml) where the computer player must implement a Minimax algorithm with alpha-beta pruning. Thus far, I understand what the goal is behind the algorithm in terms of maximizing potential gains while assuming the opponent will minify them. However, none of the resources I read helped me understand how to design the evaluation function the minimax bases all it's decisions on. All the examples have had arbitrary numbers assigned to the leaf nodes, however, I need to actually assign meaningful values to those nodes. Intuition tells me it'd be something like +1 for a win leaf node, and -1 for a loss, but how do intermediate nodes evaluate? Any help would be most appreciated.

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  • Java: micro-optimizing array manipulation

    - by Martin Wiboe
    Hello all, I am trying to make a Java port of a simple feed-forward neural network. This obviously involves lots of numeric calculations, so I am trying to optimize my central loop as much as possible. The results should be correct within the limits of the float data type. My current code looks as follows (error handling & initialization removed): /** * Simple implementation of a feedforward neural network. The network supports * including a bias neuron with a constant output of 1.0 and weighted synapses * to hidden and output layers. * * @author Martin Wiboe */ public class FeedForwardNetwork { private final int outputNeurons; // No of neurons in output layer private final int inputNeurons; // No of neurons in input layer private int largestLayerNeurons; // No of neurons in largest layer private final int numberLayers; // No of layers private final int[] neuronCounts; // Neuron count in each layer, 0 is input // layer. private final float[][][] fWeights; // Weights between neurons. // fWeight[fromLayer][fromNeuron][toNeuron] // is the weight from fromNeuron in // fromLayer to toNeuron in layer // fromLayer+1. private float[][] neuronOutput; // Temporary storage of output from previous layer public float[] compute(float[] input) { // Copy input values to input layer output for (int i = 0; i < inputNeurons; i++) { neuronOutput[0][i] = input[i]; } // Loop through layers for (int layer = 1; layer < numberLayers; layer++) { // Loop over neurons in the layer and determine weighted input sum for (int neuron = 0; neuron < neuronCounts[layer]; neuron++) { // Bias neuron is the last neuron in the previous layer int biasNeuron = neuronCounts[layer - 1]; // Get weighted input from bias neuron - output is always 1.0 float activation = 1.0F * fWeights[layer - 1][biasNeuron][neuron]; // Get weighted inputs from rest of neurons in previous layer for (int inputNeuron = 0; inputNeuron < biasNeuron; inputNeuron++) { activation += neuronOutput[layer-1][inputNeuron] * fWeights[layer - 1][inputNeuron][neuron]; } // Store neuron output for next round of computation neuronOutput[layer][neuron] = sigmoid(activation); } } // Return output from network = output from last layer float[] result = new float[outputNeurons]; for (int i = 0; i < outputNeurons; i++) result[i] = neuronOutput[numberLayers - 1][i]; return result; } private final static float sigmoid(final float input) { return (float) (1.0F / (1.0F + Math.exp(-1.0F * input))); } } I am running the JVM with the -server option, and as of now my code is between 25% and 50% slower than similar C code. What can I do to improve this situation? Thank you, Martin Wiboe

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  • Neural Network Output Grouping 0.5?

    - by Mike
    I tried to write a Neural Network system, but even running through simple AND/OR/NOR type problems, the outputs seem to group around 0.5 (for a bias of -1) and 0.7 (for a bias of 1). It doesn't look exactly "wrong"... The 1,1 in the AND pattern does seem higher than the rest and the 0,0 in the OR looks lower, but they are still all grouped so it's debatable. I was wondering a) if there's some obvious mistake I've made or b) if there's any advice for debugging Neural Nets... seeing as you can't always track back exactly where an answer came from... Thanks! Mike

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