Comparing Extreme Learning Machines (ELM) and Deep Learning (DL) to see which is better for different tasks and applications.
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In recent years, deep learning has become the go-to method for many machine learning tasks. However, there is a new kid on the block that is starting to make waves in the machine learning community: extreme learning machines (ELMs). So, which is better?
ELMs are a type of neural network that is much simpler to train than traditional deep neural networks. In fact, training an ELM requires only one forward pass through the data, and there is no need for backpropagation or any other optimization technique. This makes ELMs much faster to train than deep neural networks.
ELMs have been shown to outperform deep neural networks on some benchmark datasets. However, deep neural networks are still more widely used than ELMs due to their flexibility and ability to handle more complex data.
What is an Extreme Learning Machine?
ELM is a single-layer feedforward artificial neural network that uses a random kernel matrix to achieve fast learning. The ELM algorithm was first proposed in 2002 by Huang et al. Since then, it has been used for AdaBoost, pattern classification, regression, feature selection and other applications.
ELM has three main advantages:
-It is very fast to train. The training time scales linearly with the number of input neurons, so it is particularly well suited to large datasets.
-It can be used with any type of activation function, including non-linear ones such as sigmoid or ReLU.
-It has good generalization performance even when the data is not well-labeled or when there are outliers in the data.
There are also some disadvantages to using ELM:
-The randomness of the weights means that it is hard to replicate results exactly. This can be a problem if you need to generate reproducible results, for example in scientific research.
-The algorithm is less widely known and understood than other types of neural networks such as support vector machines (SVMs) or deep learning networks. This can make it harder to find resources and expertise if you need help with your implementation.
What is Deep Learning?
Deep learning is a subset of machine learning in which algorithms used to automatically learn and improve given experience without being explicitly programmed. Deep learning is a new area of machine learning research based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composing simple but nonlinear modules.
How do Extreme Learning Machines work?
Extreme learning machines (ELMs) are neural networks that are trained using a simple, single-step algorithm. This makes them much faster to train than deep learning networks, which can take days or even weeks to train. However, ELMs have some limitations compared to deep learning networks. They are not as good at generalizing from data and they are not as good at dealing with complex data sets.
How do Deep Learning Machines work?
Deep learning machines are similar to normal learning machines, except that they have a much larger number of hidden layers. This means that they can learn more complex patterns than a shallow learning machine, but it also means that they are more difficult to train.
There are two main types of deep learning machines: convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs are good at learn image data, while RNNs are good at learning sequential data such as text.
Both CNNs and RNNs can be used for general purpose machine learning, but CNNs tend to be better at classification tasks while RNNs tend to be better at prediction tasks.
Advantages of Extreme Learning Machines
There are many advantages of using Extreme Learning Machines (ELM) over traditional Deep Learning models. One key advantage is that ELM models can be trained much faster than Deep Learning models. This is because ELM does not require multiple passes through the data to learn the features, as Deep Learning does. ELM also does not require a large amount of data to train, so it is ideal for data sets that are small or limited.
Another advantage of ELM is that it is much simpler to train than Deep Learning models. This is because ELM only needs to learn a single set of weights, whereas Deep Learning needs to learn multiple sets of weights. This means that ELM requires less computational resources to train, and can be trained on less powerful hardware.
ELM also has a number of benefits over traditional Machine Learning algorithms. One key benefit is that ELM can handle non-linear input data, whereas most traditional Machine Learning algorithms cannot. This makes ELM ideal for problems such as image recognition, where the data is often highly non-linear.
ELM is also much better at handling noisy data than traditional Machine Learning algorithms. This is because ELM uses a random mapping function to map the input data to the hidden nodes, which means that the mapping function is less likely to be affected by noise in the data.
Finally, ELM models are often easier to interpret than Deep Learning models. This is because the weights in an ELM model are directly interpretable, whereas the weights in a Deep Learning model are often hidden inside complex layers of neurons.
Advantages of Deep Learning Machines
Deep learning machines have several advantages over extreme learning machines. First, they are able to learn from data that is less structured and more difficult to label. Second, they are better at handling complex tasks such as image recognition and natural language processing. Finally, deep learning machines are less likely to overfit the data, meaning they can generalize better to new data.
Disadvantages of Extreme Learning Machines
There are some potential disadvantages of using extreme learning machines, including:
* Requires less data to train models, which can be a problem if the data is not representative of the real world.
* Models can be more difficult to interpret than deep learning models.
* Potentially less accurate than deep learning models.
Disadvantages of Deep Learning Machines
There are several disadvantages of deep learning machines when compared to extreme learning machines. One disadvantage is that deep learning machines require a much larger amount of training data in order to learn effectively. This can be a problem when trying to learn from very limited data sets. Another issue is that deep learning machines can be inefficient when it comes to running on real-world data, due to the large number of parameters that they must learn. Finally, deep learning machines are often said to be “black boxes”, meaning that it can be difficult to understand how they arrive at their decisions.
ELM may have some advantages over deep learning, but deep learning is still the more popular choice for most applications.
Keyword: Extreme Learning Machine vs Deep Learning: Which is Better?