maksylon
• Member for 3 years
• Last seen more than a month ago
• Warszawa, Polska

I am using neural networks in industrial fault diagnosis and dynamic system modeling, so I would consider: is it a static or dynamic system? Depending on it you may choose other network structure (...

Perhabs your network is overfitting. Overfitting is where networks tuned its parameters perfectly to your training data and therefore it has very low loss on training set. Unfortunately, it will ...

Complexity Parameter CP describes a threshold $T$. If branch provides improvement less than $T$ it is deleted from the tree. You are using cp=0 so you told the algorithm to DO NOT prune any branches ...

Yes. reasoning presented here is correct. Mind that $x$ input vector is a row vector (not a column one): $[x_1, x_2, x_3]$. Multiplying $x\cdot w^1$ provides you an another row vector $y_1 = [y_{11}, ... View answer 1 votes If you are trying to do regression model you could always try to compute correlation between values. If r-Pearson correlation between input$x_i$and output$y$means that your output is similar to ... View answer 1 votes$\tanh$function is scaled standard sigmoid function$y=\frac{1}{1+e^{-ax}}$. Due to that scaling it has more steep gradient that standard sigmoid function. Steep gradient is important, because it ... View answer 1 votes You can try to compute score of matching and assign it to recognized objects. Then you could add additional label: others which will be assigned to objects which score was significantly below ... View answer 1 votes I think that classification model could be slightly better. If you are planning to use CNN to do this it would extract features which indicates vertical orientation. It would be simplier to classify ... View answer 1 votes I would try using a Genetic Algorithm to estimate optimal parameters for training. You would need to figure out the objective function of parameters of training algorithm$A_2$which minimalised ... View answer Accepted answer 0 votes I have found answer for my question here. If anyone needs it in future: all above likelihoods are assumed to be a gaussian distributions. Likelihood$P(\gamma)$is assumed to be uniform. In the ... View answer 0 votes I think the problem may caused by correlation between your predictors and by non-linear dependiences. You could try computing Pearson$r\$ linear-correlation indicator for checking each predictor-house ...

Not really. It is possible to create fuzzy logic model without setting whole ruleset. But there is a risk, that model will perform worse in some unexpected situations which would be handled by missing ...

RNN are appropriate for modeling time series. Their recursive input provides possibliity to model dynamic systems basing of their time series. I would recommend create two different models for each ...