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  • IBM enrichit sa solution de Business Intelligence Cognos Express avec Planner un nouvel outil de planification pour les PME

    IBM enrichit sa solution de Business Intelligence Cognos Express Avec Planner un nouvel outil de planification pour les PME IBM vient de lancer « Planner », un nouveau module pour sa solution d'analyse et d'informatique décisionnelle « Cognos Express ». Le module est spécialement conçu pour répondre aux besoins des moyennes entreprises. IBM Cognos Express Planner devrait offrir une démarche structurée de planification, facile à déployer et à utiliser qui permet aux utilisateurs de réagir rapidement aux conditions changeantes du marché. D'après IBM, l'interface utilisateur de Planner, simplifiée, devrait permettre aux financiers et aux non-financiers de collaborer en...

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  • The changing shape of the Business Intelligence marketplace: Applications vs. Platforms

    - by GavinPayneUK
    I recently read the latest Gartner Magic Quadrant for Business Intelligence ( link ) which put Microsoft as a leader.  However, what was more interesting for me than Microsoft’s success was how as an industry we see BI as a single marketplace, business requirement and vision, despite in my view it now being two separate areas: BI applications and BI platforms . As this article will discuss in more depth we now have two communities with differing requirements, our IT departments and our business...(read more)

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  • L'intelligence d'un réseau de voitures au service de la sécurité des automobilistes, le projet SIM devrait sauver des milliers de vies des accidents

    L'intelligence d'un réseau de voitures au service de la sécurité des automobilistes Le projet SIM devrait sauver des milliers de vies des accidents de circulationL'internet partout, celui-là même qui par définition permet d'interconnecter des objets (véhicules, hélicoptères et autres), processus et personnes, se dévoile peu à peu au grand public à travers des projets concrets à l'exemple de SIM (Safe Intelligent Mobility) de l'université des technologies du München "Technische Universität München".SIM permet de créer un réseau entre véhicules équipés de la technologie de même nom. Technologie qui d'après Cisco, devrait être compatible avec la norme sans fil 802.11p (une variante IEEE d'une norme...

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  • Les quatre tendances qui vont changer la Business Intelligence dans les trois ans à venir, selon Gartner

    Les 4 tendances qui changeront (peut-être) la BI Dans les trois ans à venir, selon Gartner Dans le cadre de ses Prédiction 2011, le cabinet Gartner a identifié quatre grandes évolutions qui devraient impacter le domaine de la Business Intelligence. Ces prévisions restent hypothétiques mais elle s'appuie tout de même sur des tendances lourdes observables. Les voici en résumé : 1 - En 2013, 33% des fonctions de BI seront consommées au travers d'appareils mobiles Le taux d'adoption et la grande disponibilité des appareils nomades, ajoutée aux efforts des éditeurs de BI (développement de nouveaux produits et marketing) devraient rapidement générer ...

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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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  • My neural network gets "stuck" while training. Is this normal?

    - by Vivin Paliath
    I'm training a XOR neural network via back-propagation using stochastic gradient descent. The weights of the neural network are initialized to random values between -0.5 and 0.5. The neural network successfully trains itself around 80% of the time. However sometimes it gets "stuck" while backpropagating. By "stuck", I mean that I start seeing a decreasing rate of error correction. For example, during a successful training, the total error decreases rather quickly as the network learns, like so: ... ... Total error for this training set: 0.0010008071327708653 Total error for this training set: 0.001000750550254843 Total error for this training set: 0.001000693973929822 Total error for this training set: 0.0010006374037948094 Total error for this training set: 0.0010005808398488103 Total error for this training set: 0.0010005242820908169 Total error for this training set: 0.0010004677305198344 Total error for this training set: 0.0010004111851348654 Total error for this training set: 0.0010003546459349181 Total error for this training set: 0.0010002981129189812 Total error for this training set: 0.0010002415860860656 Total error for this training set: 0.0010001850654351723 Total error for this training set: 0.001000128550965301 Total error for this training set: 0.0010000720426754587 Total error for this training set: 0.0010000155405646494 Total error for this training set: 9.99959044631871E-4 Testing trained XOR neural network 0 XOR 0: 0.023956746649767453 0 XOR 1: 0.9736079194769579 1 XOR 0: 0.9735670067093437 1 XOR 1: 0.045068688874314006 However when it gets stuck, the total errors are decreasing, but it seems to be at a decreasing rate: ... ... Total error for this training set: 0.12325486644721295 Total error for this training set: 0.12325486642503929 Total error for this training set: 0.12325486640286581 Total error for this training set: 0.12325486638069229 Total error for this training set: 0.12325486635851894 Total error for this training set: 0.12325486633634561 Total error for this training set: 0.1232548663141723 Total error for this training set: 0.12325486629199914 Total error for this training set: 0.12325486626982587 Total error for this training set: 0.1232548662476525 Total error for this training set: 0.12325486622547954 Total error for this training set: 0.12325486620330656 Total error for this training set: 0.12325486618113349 Total error for this training set: 0.12325486615896045 Total error for this training set: 0.12325486613678775 Total error for this training set: 0.12325486611461482 Total error for this training set: 0.1232548660924418 Total error for this training set: 0.12325486607026936 Total error for this training set: 0.12325486604809655 Total error for this training set: 0.12325486602592373 Total error for this training set: 0.12325486600375107 Total error for this training set: 0.12325486598157878 Total error for this training set: 0.12325486595940628 Total error for this training set: 0.1232548659372337 Total error for this training set: 0.12325486591506139 Total error for this training set: 0.12325486589288918 Total error for this training set: 0.12325486587071677 Total error for this training set: 0.12325486584854453 While I was reading up on neural networks I came across a discussion on local minimas and global minimas and how neural networks don't really "know" which minima its supposed to be going towards. Is my network getting stuck in a local minima instead of a global minima?

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  • Reinforcement learning toy project

    - by Betamoo
    My toy project to learn & apply Reinforcement Learning is: - An agent tries to reach a goal state "safely" & "quickly".... - But there are projectiles and rockets that are launched upon the agent in the way. - The agent can determine rockets position -with some noise- only if they are "near" - The agent then must learn to avoid crashing into these rockets.. - The agent has -rechargable with time- fuel which is consumed in agent motion - Continuous Actions: Accelerating forward - Turning with angle I need some hints and names of RL algorithms that suit that case.. - I think it is POMDP , but can I model it as MDP and just ignore noise? - In case POMDP, What is the recommended way for evaluating probability? - Which is better to use in this case: Value functions or Policy Iterations? - Can I use NN to model environment dynamics instead of using explicit equations? - If yes, Is there a specific type/model of NN to be recommended? - I think Actions must be discretized, right? I know it will take time and effort to learn such a topic, but I am eager to.. You may answer some of the questions if you can not answer all... Thanks

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  • Algorithm to generate numerical concept hierarchy

    - by Christophe Herreman
    I have a couple of numerical datasets that I need to create a concept hierarchy for. For now, I have been doing this manually by observing the data (and a corresponding linechart). Based on my intuition, I created some acceptable hierarchies. This seems like a task that can be automated. Does anyone know if there is an algorithm to generate a concept hierarchy for numerical data? To give an example, I have the following dataset: Bangladesh 521 Brazil 8295 Burma 446 China 3259 Congo 2952 Egypt 2162 Ethiopia 333 France 46037 Germany 44729 India 1017 Indonesia 2239 Iran 4600 Italy 38996 Japan 38457 Mexico 10200 Nigeria 1401 Pakistan 1022 Philippines 1845 Russia 11807 South Africa 5685 Thailand 4116 Turkey 10479 UK 43734 US 47440 Vietnam 1042 for which I created the following hierarchy: LOWEST ( < 1000) LOW (1000 - 2500) MEDIUM (2501 - 7500) HIGH (7501 - 30000) HIGHEST ( 30000)

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  • How can I extract similarities/patterns from a collection of binary strings?

    - by JohnIdol
    I have a collection of binary strings of given size encoding effective solutions to a given problem. By looking at them, I can spot obvious similarities and intuitively see patterns of symmetry and periodicity. Are there mathematical/algorithmic tools I can "feed" this set of strings to and get results that might give me an idea of what this set of strings have in common? By doing so I would be able to impose a structure (or at least favor some features over others) on candidate solutions in order to greatly reduce the search space, maximizing chances to find optimal solutions for my problem (I am using genetic algorithms as the search tool - but this is not pivotal to the question). Any pointers/approaches appreciated.

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  • Algorithm shortest path between all points

    - by Jeroen
    Hi, suppose I have 10 points. I know the distance between each point. I need to find the shortest possible route passing trough all points. I have tried a couple of algorithms (Dijkstra, Floyd Warshall,...) and the all give me the shortest path between start and end, but they don't make a route with all points on it. Permutations work fine, but they are to resource expensive. What algorithms can you advise me to look into for this problem? Or is there a documented way to do this with the above mentioned algorithms? Tnx Jeroen

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  • Neural Networks in C# using NeuronDotNet

    - by kingrichard2005
    Hello, I'm testing the NeuronDotNet library for a class assignment using C#. I have a very simple console application that I'm using to test some of the code snippets provided in the manual fro the library, the goal of the assignment is to teach the program how to distinguish between random points in a square which may or may not be within a circle that is also inside the square. So basically, which points inside the square are also inside the circle. Here is what I have so far: namespace _469_A7 { class Program { static void Main(string[] args) { //Initlaize the backpropogation network LinearLayer inputLayer = new LinearLayer(2); SigmoidLayer hiddenLayer = new SigmoidLayer(8); SigmoidLayer outputLayer = new SigmoidLayer(2); new BackpropagationConnector(inputLayer, hiddenLayer); new BackpropagationConnector(hiddenLayer, outputLayer); BackpropagationNetwork network = new BackpropagationNetwork(inputLayer, outputLayer); //Generate a training set for the ANN TrainingSet trainingSet = new TrainingSet(2, 2); //TEST: Generate random set of points and add to training set, //for testing purposes start with 10 samples; Point p; Program program = new Program(); //Used to access randdouble function Random rand = new Random(); for(int i = 0; i < 10; i++) { //These points will be within the circle radius Type A if(rand.NextDouble() > 0.5) { p = new Point(rand.NextDouble(), rand.NextDouble()); trainingSet.Add(new TrainingSample(new double[2] { p.getX(), p.getY() }, new double[2] { 1, 0 })); continue; } //These points will either be on the border or outside the circle Type B p = new Point(program.randdouble(1.0, 4.0), program.randdouble(1.0, 4.0)); trainingSet.Add(new TrainingSample(new double[2] { p.getX(), p.getY() }, new double[2] { 0, 1 })); } //Start network learning network.Learn(trainingSet, 100); //Stop network learning //network.StopLearning(); } //generates a psuedo-random double between min and max public double randdouble(double min, double max) { Random rand = new Random(); if (min > max) { return rand.NextDouble() * (min - max) + max; } else { return rand.NextDouble() * (max - min) + min; } } } //Class defines a point in X/Y coordinates public class Point { private double X; private double Y; public Point(double xVal, double yVal) { this.X = xVal; this.Y = yVal; } public double getX() { return X; } public double getY() { return Y; } } } This is basically all that I need, the only question I have is how to handle output?? More specifically, I need to output the value of the "step size" and the momentum, although it would be nice to output other information as well. Anyone with experience using NeuronDotNet, your input is appreciated.

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  • Face Recognition for classifying digital photos?

    - by Jeremy E
    I like to mess around with AI and wanted to try my hand at face recognition the first step is to find the faces in the photographs. How is this usually done? Do you use convolution of a sample image/images or statistics based methods? How do you find the bounding box for the face? My goal is to classify the pictures of my kids from all the digital photos. Thanks in advance.

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  • Nominal Attributes in LibSVM

    - by Chris S
    When creating a libsvm training file, how do you differentiate between a nominal attribute verses a numeric attribute? I'm trying to encode certain nominal attributes as integers, but I want to ensure libsvm doesn't misinterpret them as numeric values. Unfortunately, libsvm's site seems to have very little documentation. Pentaho's docs seem to imply libsvm makes this distinction, but I'm still not clear how it's made.

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  • Dont understand Python Method

    - by user836087
    I dont understand what is going on in the move method. I am taking the AI course from Udacity.com. The video location is: http://www.udacity.com/view#Course/cs373/CourseRev/apr2012/Unit/512001/Nugget/480015 Below is the code I dont get, its not working as shown in the video .. The answer I should be getting according to Udacity is [0, 0, 1, 0, 0] Here is what I get [] p=[0, 1, 0, 0, 0] def move(p, U): q = [] for i in range(len(p)): q.append(p[(i-U) % len(p)]) return q print move(p, 1)

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