Is there any way to use trained neural network using "SOM toolbox" for classification of data in data set? For example I have data, I put it to the network and network tells me the type of data.
Related Questions in MATLAB
- Convert Cell Array of Symbolic Functions to Double Array of Symbolic Functions MATLAB
- How to restrict vpasolve() to only integer solutions (MATLAB)
- "Error in port widths or dimensions" while producting 27
- matlab has encountered an internal problem needs to close
- Minimize the sum of squared errors between the experimental and predicted data in order to estimate two optimum parameters by using matlab
- Solve equation with Crank Nicolson and Newton iterative method in Matlab
- Why options are not available in EEGLAB menu options?
- ash: ./MathWorksProductInstaller: not found, but file exists
- iterative GA optimization algorithm
- Create Symbolic Function from Double Vector MATLAB
- Fixing FEA Model loading with correct units and stress results
- loading variables from a python script in matlab
- Why cannot I set font of `xlabel` in `plotmf` in MATLAB?
- How would I go about filtering non-standardly formatted serial data which contains some junk binary between data entries?
- Cyclic Voltammetry Simmulation in MATLAB, I am running into issues with my data points returning as NaN values, i am a beginner, any help wanted
Related Questions in NEURAL-NETWORK
- Influence of Unused FFN on Model Accuracy in PyTorch
- How to train a model with CSV files of multiple patients?
- Does tensorflow have a way of calculating input importance for simple neural networks
- My ICNN doesn't seem to work for any n_hidden
- a problem for save and load a pytorch model
- config QConfig in pytorch QAT
- How can I convert a flax.linen.Module to a torch.nn.Module?
- Spiking neural network on FPGA
- Error while loading .keras model: Layer node index out of bounds
- Matrix multiplication issue in a Bidirectional LSTM Model
- Recommended way to use Gymnasium with neural networks to avoid overheads in model.fit and model.predict
- Loss is not changing. Its remaining constant
- Relationship Between Neural Network Distances and Performance
- Mapping a higher dimension tensor into a lower one: (B, F, D) -> (B, F-n, D) in PyTorch
- jax: How do we solve the error: pmap was requested to map its argument along axis 0, which implies that its rank should be at least 1, but is only 0?
Related Questions in SELF-ORGANIZING-MAPS
- Observation Labels in Kohonen Map in R
- How to calculate the topographic error fur SuperSOM made with the kohonen package?
- Solving subset sum problem on superincreasing list using a Self Organizing Map (SOM)
- How do you know which is the closest class example(observation) to a class in Self Organizing Map?
- Evaluate performance of Self-organizing map for classification
- Is there a way to obtain the same results of numpy function with tensorflow tensors?
- How does the number of iterations affect convergence in a self-organizing map?
- Example Java code for Kohonen filter using apache.commons.math.ml
- Transform 2d plot into 1d plot
- Regression with Self Organizing Map (SOM) / Kohonen Map
- How to define neighborhoods in hexagonal Self-Organizing Maps (SOM)
- How to calculate the distance between a random point in a dataset and a center for the kohonen algorithm?
- Tableau: Self-Organizing Map visualization
- Convert dataframe to matrix list
- Library sompy for implementing self-organizing maps
Popular Questions
- How do I undo the most recent local commits in Git?
- How can I remove a specific item from an array in JavaScript?
- How do I delete a Git branch locally and remotely?
- Find all files containing a specific text (string) on Linux?
- How do I revert a Git repository to a previous commit?
- How do I create an HTML button that acts like a link?
- How do I check out a remote Git branch?
- How do I force "git pull" to overwrite local files?
- How do I list all files of a directory?
- How to check whether a string contains a substring in JavaScript?
- How do I redirect to another webpage?
- How can I iterate over rows in a Pandas DataFrame?
- How do I convert a String to an int in Java?
- Does Python have a string 'contains' substring method?
- How do I check if a string contains a specific word?
Trending Questions
- UIImageView Frame Doesn't Reflect Constraints
- Is it possible to use adb commands to click on a view by finding its ID?
- How to create a new web character symbol recognizable by html/javascript?
- Why isn't my CSS3 animation smooth in Google Chrome (but very smooth on other browsers)?
- Heap Gives Page Fault
- Connect ffmpeg to Visual Studio 2008
- Both Object- and ValueAnimator jumps when Duration is set above API LvL 24
- How to avoid default initialization of objects in std::vector?
- second argument of the command line arguments in a format other than char** argv or char* argv[]
- How to improve efficiency of algorithm which generates next lexicographic permutation?
- Navigating to the another actvity app getting crash in android
- How to read the particular message format in android and store in sqlite database?
- Resetting inventory status after order is cancelled
- Efficiently compute powers of X in SSE/AVX
- Insert into an external database using ajax and php : POST 500 (Internal Server Error)
I do not know if you are asking for any specific SOM toolbox but I will give a general idea.
First of all it is possible to use SOM to classify data as long as you have some labelled training data, or otherwise you classify each node of the network in one specific class:
In the first case, you train your network with both inputs and desired result as features. After learning, you give the new test data to classify with only the inputs (without the desired result). The network give you back which was the best matching unit, and with this you can access to which desired result it corresponds.
In the second case you train your network in the normal way (with only the inputs as features). You classify the different network nodes. After learning, you give the new test data to classify again with only the inputs. The network give you back which was the best matching unit, and with this you can access to which class it points to.
The second case should be straightforward in any toolbox, yet the first one is not. For the first case my simple suggestion (without coding yourself) would be to train 2 networks: one with both the inputs and desired result as features, and one with only the inputs. Replace the weights discovered by the learning process of the network with only the inputs, with the ones from the inputs+result and now you can use this as normal. Get the BMU and point to the respective class.