artificial-intelligence

SVM and Neural Network

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问题 What is difference between SVM and Neural Network? Is it true that linear svm is same NN, and for non-linear separable problems, NN uses adding hidden layers and SVM uses changing space dimensions? 回答1: There are two parts to this question. The first part is "what is the form of function learned by these methods?" For NN and SVM this is typically the same. For example, a single hidden layer neural network uses exactly the same form of model as an SVM. That is: Given an input vector x, the

Intelligent agents “tutorial” [closed]

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Closed. This question is off-topic. It is not currently accepting answers. Learn more . Want to improve this question? Update the question so it's on-topic for Stack Overflow. I've recently come across Intelligent Agents by reading this book : link text I'm interested in finding a good book for beginners, so I can start to implement such a system. I've also tried reading "Multiagent Systems : A modern approach to distributed artificial intelligence" (can't find it on amazon) but it's not what I'm looking for. Thanks for the help :). The agent view point is simply an abstraction of convenience.

Searching Natural Language Sentence Structure

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What's the best way to store and search a database of natural language sentence structure trees? Using OpenNLP's English Treebank Parser, I can get fairly reliable sentence structure parsings for arbitrary sentences. What I'd like to do is create a tool that can extract all the doc strings from my source code, generate these trees for all sentences in the doc strings, store these trees and their associated function name in a database, and then allow a user to search the database using natural language queries. So, given the sentence "This uploads files to a remote machine." for the function

What are some impressive algorithms or software in the world of AI?

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问题 I have always loved the idea of AI and evolutionary algorithms. Unfortunately, as we all know, the field hasn't developed nearly as fast as expected in the early days. What I am looking for are some examples that have the "wow" factor: Self-directed learning systems that adapted in unexpected ways. Game agents that were particularly dynamic and produced unexpected strategies Symbolic representation systems that actually produced some meaningful and insightful output Interesting emergent

Algorithm to find related words in a text

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I would like to have a word (e.g. "Apple) and process a text (or maybe more). I'd like to come up with related terms. For example: process a document for Apple and find that iPod, iPhone, Mac are terms related to "Apple". Any idea on how to solve this? As a starting point: your question relates to text mining . There are two ways: a statistical approach, and one form natural language processing (nlp). I do not know much about nlp, but can say something about the statistical approach: You need some vector space representation of your documents, see http://en.wikipedia.org/wiki/Vector_space

Why is the complexity of A* exponential in memory?

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Wikipedia says on A* complexity the following ( link here ): More problematic than its time complexity is A*’s memory usage. In the worst case, it must also remember an exponential number of nodes. I fail to see this is correct because: Say we explore node A, with successors B, C, and D. Then we add B, C, and D to the list of open nodes, each accompanied by a reference to A, and we move A from the open nodes to the closed nodes. If at some time we find another path to B (say, via Q), that is better than the path through A, then all that is needed is to change B's reference to A to point to Q

My own OCR-program in Python

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问题 I am still a beginner but I want to write a character-recognition-program. This program isn't ready yet. And I edited a lot, therefor the comments may not match exactly. I will use the 8-connectivity for the connected component labeling. from PIL import Image import numpy as np im = Image.open("D:\\Python26\\PYTHON-PROGRAMME\\bild_schrift.jpg") w,h = im.size w = int(w) h = int(h) #2D-Array for area area = [] for x in range(w): area.append([]) for y in range(h): area[x].append(2) #number 0 is

Algorithm to understand meaning [closed]

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I want to know if is there any specific algorithm that can be followed to understand the meaning of a word/sentence/paragraph. Basically, I want to write a program that will take text/paragraph as input and try to find out what its meaning is. And thereby highlight the emotions within the text. Also, if there is an algorithm to understand things, can the same algorithm be applied to itself? It reduces the quest further to a point where we become interested in knowing meaning of meaning OR rather definition of definition. You want Natural Language Processing and Semantic Technology . This is

How can I program a simple chat bot AI?

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问题 I want to build a bot that asks someone a few simple questions and branches based on the answer. I realize parsing meaning from the human responses will be challenging, but how do you setup the program to deal with the "state" of the conversation? It will be a one-to-one conversation between a human and the bot. 回答1: You probably want to look into Markov Chains as the basics for the bot AI. I wrote something a long time ago (the code to which I'm not proud of at all, and needs some mods to

Tutorials For Natural Language Processing [closed]

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问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 3 years ago . I recently attended a class on coursera about "Natural Language Processing" and I learnt a lot about parsing, IR and other interesting aspects like Q&A etc. though I grasped the concepts well but I did not actually get any practical knowledge of it. Can anyone suggest me good online tutorials or books for