artificial-intelligence

问题 What is the difference between stochastic hill climbing and first-choice hill climbing algorithms? 回答1: Hill Climbing Search Algorithm is one of the family of local searches that move based on the better states of its neighbors. Stochastic Hill Climbing chooses a random better state from all better states in the neighbors while first-choice Hill Climbing chooses the first better state from randomly generated neighbors. First-Choice Hill Climbing will become a good strategy if the current

问题 What is the difference between stochastic hill climbing and first-choice hill climbing algorithms? 回答1: Hill Climbing Search Algorithm is one of the family of local searches that move based on the better states of its neighbors. Stochastic Hill Climbing chooses a random better state from all better states in the neighbors while first-choice Hill Climbing chooses the first better state from randomly generated neighbors. First-Choice Hill Climbing will become a good strategy if the current

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 4 years ago . I'm looking for some OS java engines for medical diseases diagnose . these are engines that takes queries input from user discribing patient symptoms and the engine should return suggestions of potential disease according to input symptoms. does such engines exists somewhere? I prefer some Java OS engine in this

问题 Let's say I have filenames which are formatted differently. I want to be able to extract certain aspects from said filename like a human would; pattern recognition. Obviously I can bruteforce myself through with regular expressions but that's not what I'm after. Let's say I have these 4 strings: [MAS] Hayate no Gotoku!! 20 [BD 720p] [21D138F8].mkv [Leopard-Raws] Akatsuki no Yona - 05 RAW (MX 1280x720 x264 AAC).mp4 [BLAST] Wolf Girl and Black Prince - 05 [720p] [C1252A5E].mkv [sage]_Mobile

问题 I have read a fair amount about Haar training and I'm not clear on how many images one should use for the positive and negative sample sets. I see it recommended to use many images, some people recommend thousands. I'm also unclear of whether the number of positive and negative sample images should be the same? 回答1: Here is the best tutorial on Haar training. Have you tried this? http://note.sonots.com/SciSoftware/haartraining.html It says they used 5000 for positive and 3000 for negative.

问题 How many times do I use a sample of training data in one training cycle? Say I have 60 training data. I go through the 1st row and do a forward pass and adjust weights using results from backward pass. Using the sigmoidal function as below: Forward pass Si = sum of (Wi * Uj) Ui = f(Si) = 1 / 1 + e^ - Si Backward pass Output Cell = (expected -Ui)(f'(Si)), where f'(Si) = Ui(1-Ui) Do I then go through the 2nd row and do the same process as the 1st or do I go around the 1st row until the error is

问题 i am writing an A* algorithm which can solve the 8-puzzle in Java, so far i have implemented DFS, BFS, A* using the number of tiles out of place and i just need to implement it using the heuristic for the Manhattan distance. As you are probably aware the Manhattan distance is the sum of each tiles displacement in relation to its current position and its index in the goal state. I have googled around and found these stack over flow topics: Calculating Manhattan Distance Manhattan distance in A

问题 I have just solved the nqueen problem in python. The solution outputs the total number of solutions for placing n queens on an nXn chessboard but trying it with n=15 takes more than an hour to get an answer. Can anyone take a look at the code and give me tips on speeding up this program...... A novice python programmer. #!/usr/bin/env python2.7 ############################################################################## # a script to solve the n queen problem in which n queens are to be

问题 I have a simple machine learning question: I have n (~110) elements, and a matrix of all the pairwise distances. I would like to choose the 10 elements that are most far apart. That is, I want to Maximize: Choose 10 different elements. Return min distance over (all pairings within the 10). My distance metric is symmetric and respects the triangle inequality. What kind of algorithm can I use? My first instinct is to do the following: Cluster the n elements into 20 clusters. Replace each

问题 How do you find an optimum learning rule for a given problem, say a multiple category classification? I was thinking of using Genetic Algorithms, but I know there are issues surrounding performance. I am looking for real world examples where you have not used the textbook learning rules, and how you found those learning rules. 回答1: Nice question BTW . classification algorithms can be classified using many Characteristics like: What does the algorithm strongly prefer (or what type of data that