Radial Basis Function Network (RBF Network)
According to the link below, i didn't understand what is the "center vector for neuron i", in other words "center of the RBF units called also prototype ".
read the Network architecture in this link
Please i need your appreciated help.
If you read on to the Training section in your link, it explains what the centre vectors are:
Reading the above, it seems to me that you have your set of samples, the x's, and from these you choose a number of centre vectors - one for each neuron in the hidden layer . The centre vectors, broadly speaking are centres of clusters in your sample data.
As the remarks say, you can use an unsupervised clustering algorithm, such as k-means, to find n cluster centres in your data, where n is the number of neurons in the hidden layer that you are dealing with. Different layers may have more or less neurons and so will have correspondingly more or less centre vectors.
Then the RBF relates each individual sample, x, to each centre vector by some function of the Euclidean distance between them.
- scikit-learn cross-validation over-fitting or under-fitting
- XGBoost/ XGBRanker to produce probabilities instead of ranking scores
- Classification using Graph Neural Network
- How to process requests from multiiple users using ML model and FastAPI?
- The feature names should match those that were passed during fit
- 'super' object has no attribute '__sklearn_tags__'
- Can we use GNN on graphs with only edge features?
- How do I initialize weights in PyTorch?
- Pytorch GRU error RuntimeError : size mismatch, m1: [1600 x 3], m2: [50 x 20]
- What's the major difference between glove and word2vec?
- How to use a Dense layer with an input that has a dynamically sized dimension?
- Optuna Hyperband Algorithm Not Following Expected Model Training Scheme
- What is the difference between Google Cloud Vision API and Mobile Vision?
- Keras ValueError: The layer sequential has never been called and thus has no defined output
- Deep-Learning Nan loss reasons
- Databricks MLFlow and MetaFlow integration
- Getting "TypeError: ufunc 'isnan' not supported for the input types"
- TypeError: '_IncompatibleKeys' object is not callable
- Does it make sense to use Conda + Poetry?
- Does pytorch CNN care about image size?
- "Found input variables with inconsistent numbers of samples" Have I done something wrong during the train_test_split?
- Feature Selection in PySpark
- tidymodels workflow errors when trying to use workflow_map()
- Integrating numerical/physical data for CNN image classification
- pytorch torchvision.datasets.ImageFolder FileNotFoundError: Found no valid file for the classes .ipynb_checkpoints
- How do I apply one hot encoding on a pandas dataframe with both categorical and numerical features?
- ValueError: could not convert string to float: 'N/'
- Holdout validation set- hyperparameter tuning
- NotFittedError: This DecisionTreeClassifier instance is not fitted yet
- Principal component analysis