What is a machine learning algorithm
"Machine learning" can be translated simply with "machine learning". This already gives rise to the importance of machine learning, namely the ability of machines to learn independently. In the sense of an artificial generation of knowledge from experience. The computer system doesn't just learn examples by heart, it recognizes patterns. By applying certain procedures, knowledge can not only be acquired independently, but also expanded. In this blog we want to devote ourselves to the various methods and learning processes that ensure that we receive product recommendations on Amazon or that the machine in production operation reports that it will soon require maintenance.
Behind every artificial system there is a special learning method, certain rules or algorithms. In the following, we provide an insight into the numerous approaches machines can use to learn:
1. Association Rule Learning
Association Rule Learning is a method to uncover relevant correlations between variables in large databases.
2. Artificial Neural Networks
A Artificial Neural Network Learning Algorithm (ANN) or neural network (NN) is a learning algorithm, which in its structure and its functional aspects
is inspired by natural neural networks. Neural networks are made up of interconnected artificial neurons that use information
a connectionist approach.
3. Deep learning
Deep learning is made up of various hidden layers in a neural network. This approach tries to get the kind of
model how the human brain sees light and hears sound. Examples of successful deep learning applications are computer vision and
4. Inductive logic programming
Inductive logic programming (Inductive Logical Programming, ILP) is an approach to rule learning in which logical programming
is used as a uniform feature of input examples, background knowledge and hypotheses. An ILP system derives from coded background information and a series of exemplary facts in the format of a logical database a hypothetical logical program that contains only positive and no negative examples.
5. Support vector machines
Support Vector Machines (SVMs) include a number of related supervised learning methods for classification and regression purposes. Using a few learning examples assigned to one of two categories, an SMV algorithm creates a model that predicts which category a new example will be assigned to.
Cluster analysis is the assignment of a series of observations to subsets (clusters) so that observations within the same cluster are similar based on previously defined criteria, while observations from different clusters are not similar.
7. Bayesian Networks
A Bayesian Network, also Bayesian network or directed acyclic graph, describes a probabilistic graphical model that contains a series of random
Represents variables and their conditional dependencies via a directed acyclic graph (DAG). For example, a Bayesian network could represent the likely relationships between diseases and symptoms.
8. Reinforcement learning
Reinforcement learning or reinforcement learning is how an agent must act in an environment in order to maximize the rewards received. Reinforcement learning algorithms try to find a strategy that assigns states to the actions that the agent must take in those states.
9. Representation learning
Various, mostly unsupervised, learning algorithms have the goal of recognizing better features from the inputs provided during the learning phase.
Classic examples are principal component analysis and cluster analysis. Representation learning algorithms often try to preserve the information in the inputs but transform it so that it is useful.
10. Similarity and metric learning
To solve this problem, similar sample pairs and less similar objects are provided to the learning system. The system needs to learn a similarity function (or distance function) that can predict whether objects are similar or dissimilar.
Read here where machine learning is currently being used!
Which algorithm for machine learning is the right one? Unfortunately, like so many others, this question can only be answered with a "It really depends".
It depends on what purpose you are pursuing with the data obtained, what quantity, quality and consistency the data have and much more. You are welcome to contact us in order to specify "that depends". In our whitepaper "Location determination - intelligent data"Let's go into the goals that can be achieved with data. Download it now!
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