artificial-intelligence

Neural Net Bias per Layer or per Node (non-input node)

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问题 I am looking to implement a generic Neural Net, with 1 Input Layer consisting of Input Nodes, 1 Output Layer consisting of Output Nodes, and N Hidden Layers consisting of Hidden Nodes. Nodes are organized into Layers, with the rule that Nodes in the same Layer cannot be connected. I mostly understand the concept of the Bias, and my question is this: Should there be one Bias value per Layer (shared by all nodes in that Layer) or should each Node (except Nodes in the Input Layer) have their own

Combining Weak Learners into a Strong Classifier

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How do I combine few weak learners into a strong classifier? I know the formula, but the problem is that in every paper about AdaBoost that I've read there are only formulas without any example. I mean - I got weak learners and their weights, so I can do what the formula tells me to do (multiply learner by its weight and add another one multiplied by its weight and another one etc.) but how exactly do I do that? My weak learners are decision stumps. They got attribute and treshold, so what do I multiply? If I understand your question correctly, you have a great explanation on how boosting

Datasets to test Nonlinear SVM

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I'm implementing a nonlinear SVM and I want to test my implementation on a simple not linearly separable data. Google didn't help me find what I want. Can you please advise me where I can find such data. Or at least, how can I generate such data manually ? Thanks, Well, SVMs are two-class classifiers--i.e., these classifiers place data on either side of a single decision boundary. Therefore, i would suggest a data set comprised of just two classes (that's not strictly necessary because of course an SVM can separate more than two classes by passing the Classifier multiple times (in series) over

What is the difference between Deep Learning and traditional Artificial Neural Network machine learning? [closed]

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Closed . This question needs to be more focused. It is not currently accepting answers. Learn more . Want to improve this question? Update the question so it focuses on one problem only by editing this post . Can you offer a concise explanation of the differences between Deep Learning and Traditional Machine Learning that utilize neural networks? How many levels are need to make a neural network "deep"? Is this all just marketing hype? I beg to differ with @Frank Puffer's answer. I don't understand what he meant by performing an unsupervised learning procedure on the hidden layers etc. Deep

Brain modelling

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问题 Just wondering, since we've reached 1 teraflop per PC, yet we are still not able to model an insect's brain. Has anyone seen a decent implementation of a self-learning, self-developing neural network? 回答1: I saw an interesting experiment mapping the physical neural layout of a rat's brain to a digital neural network with weighting modelled on the neuron chemistry of each component taken using MRI and others. Quite interesting. (new scientist or Focus, 2 issues ago?) IBM Blue Brain comes to

F# and Fuzzy Logic

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I know it might sound strange but I would like to know one thing in this new world where Microsoft Visual F# is getting into. There are many application of this language, I am going to learn, regarding parsing, functional programming, structured programming... But what about artificial intelligence? Are there any applications for Fuzzy Logic? Is F# a good language to be used for Fuzzy Logic applications? At university we are studying Prolog and similar languages. Prolog is able to create complex query in a very plain and short expresisons (by taking advantage of predicates and facts). Is F#

What is the difference between SOM (Self Organizing Maps) and K-Means?

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问题 There is only one question related to this in stackoverflow, and it is more about which one is better. I just dont really understand the difference. I mean they both work with vectors, which are assigned randomly to clusters, they both work with the centroids of the different clusters in order to determine the winning output node. I mean, where exactly lies the difference? 回答1: In K-means the nodes (centroids) are independent from each other. The winning node gets the chance to adapt each

Can someone explain Artificial Neural Networks? [closed]

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It's difficult to tell what is being asked here. This question is ambiguous, vague, incomplete, overly broad, or rhetorical and cannot be reasonably answered in its current form. For help clarifying this question so that it can be reopened, visit the help center . According to Wikipedia (which is a bad source, I know) A neural network is comprised of An input layer of A neurons Multiple (B) Hidden layers each comprised of C neurons. An output layer of "D" neurons. I understand what does input and output layers mean. My question is how to determine an optimal amount of layers and neuron-per

How can I check if one game object can see another?

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I have an object, that is facing a particular direction with (for instance) a 45 degree field of view, and a limit view range. I have done all the initial checks (Quadtree node, and distance), but now I need to check if a particular object is within that view cone, (In this case to decide only to follow that object if we can see it). Apart from casting a ray for each degree from Direction - (FieldOfView / 2) to Direction + (FieldOfView / 2) (I am doing that at the moment and it is horrible), what is the best way to do this visibility check? Compute the angle between your view direction

Finding minimum cut-sets between bounded subgraphs

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If a game map is partitioned into subgraphs, how to minimize edges between subgraphs? I have a problem, Im trying to make A* searches through a grid based game like pacman or sokoban, but i need to find "enclosures". What do i mean by enclosures? subgraphs with as few cut edges as possible given a maximum size and minimum size for number of vertices for each subgraph that act as a soft constraints. Alternatively you could say i am looking to find bridges between subgraphs, but its generally the same problem. Example Gridbased gamemap example http://dl.dropbox.com/u/1029671/map1.jpg Given a