Items tagged “ai”
18 results found
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Computer aided diagnosis
Computer aided diagnosis (CAD) is the use of a computer generated output as an assisting tool for a clinician to make a diagnosis. It is different from automated computer diagnosis, in which the end diagnosis is based on a computer algorithm only.
As an early form of artificial intelligence, co...
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Support vector machine (machine learning)
The support vector machine (SVM) is a supervised learning algorithm used to separate groups of data with a margin or plane which is made as well as possible to ensure it is more likely to generalize well to examples it has never seen before. In the case of a two feature data set a margin or line...
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Feature scaling
Feature scaling a preprocessing technique that is used to standardize the range of values in data features, making sure that the features are on a similar scale. It is used when the range of values of a certain feature is too variable and contains extreme values as most algorithms perform poorly...
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Gradient descent
The gradient descent algorithm is an optimization strategy that aims to minimize an objective cost function (degree of predicting error) of a model in order to produce a model that gives the most accurate predictions. Gradient descent is by far the most commonly used algorithm in machine learnin...
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Stochastic gradient descent
Stochastic gradient descent is an optimization algorithm which improves the efficiency of the gradient descent algorithm. Similar to batch gradient descent, stochastic gradient descent performs a series of steps to minimize a cost function. Unlike batch gradient descent, which is computationally...
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Mini-batch gradient descent
The mini-batch gradient descent is a technique that combines properties from batch gradient descent and also stochastic gradient descent to optimize efficiency and accuracy of the gradient descent algorithm. In each iteration, a certain number of examples (a batch) within a data set will undergo...
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Regularisation (Regularization)
Regularisation is a process of reducing the complexity of a model through the inclusion of an additional parameter as in order to reduce the overfitting of a model to the training data.
In the context of radiology, a common model type used to interpret images is the convolutional neural network...
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Anomaly detection
Anomaly detection finds statistical outliers in data. Machine learning based anomaly detection algorithms use a large number of normal examples to train an algorithm which detects what is normal (based on the training examples) and what is not normal. Anomaly detection algorithms can have featur...
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Dimensionality reduction
Dimensionality reduction is the process of combining the information from a large number of features to a create a smaller number of features, either to reduce the computational cost or to visualize the data.
In order to achieve the most accurate result, it is often required to have many featur...
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Validation split (machine learning)
In order to ensure that machine learning models are able to generalize well to new data not seen before by the model, it is important to have several sets of data including training data, test data, and cross-validation split data for the original set of data to obtain the best possible predicti...
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Data augmentation
Data augmentation is a technique that increases the amount of data by adding slightly modified copies of already existing data. This increases the diversity of the training set, which helps to reduce overfitting when training a machine learning model and can have a positive effect on the model's...
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Natural language processing
Natural language processing (NLP) is an area of active research in artificial intelligence concerned with human languages. Natural language processing programs use human written text or human speech as data for analysis. The goals of natural language processing programs can vary from generating ...
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Bayes' theorem
Bayes' theorem, also known as Bayes' rule or Bayes' law, is a theorem in statistics that describes the probability of one event or condition as it relates to another known event or condition. Mathematically, the theory can be expressed as follows: P(A|B) = (P(B|A) x P(A) )/P(B), where given that...
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Python (programming language)
Python is a high-level, general-purpose computer programming language. Initially, Python was created by Dutch computer programmer Guido van Rossum and was first released in 1991. The version 3.7.4 (which is the most recent stable release as of July 2019) Python language has objects and associat...
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Image normalization
Image normalization is a process, often used in the preparation of data sets for artificial intelligence (AI), in which multiple images are put into a common statistical distribution in terms of size and pixel values; however, a single image can also be normalized within itself. The process usua...
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Bayes' factor
A Bayes' factor is a number that quantifies the relative likelihood of two models or hypotheses to each other if made into a ratio e.g. if two models are equally likely based on the prior evidence ( or there is no prior evidence) then the Bayes factor would be one.
Such factors have several use...
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Hyperparameter (machine learning)
Hyperparameters are specific aspects of a machine learning algorithm that are chosen before the algorithm runs on data. These hyperparameters are model specific e.g. they would typically include the number of epochs for a deep learning model or the number of branches in a decision tree model. Th...
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Hebbian learning
Hebbian learning describes a type of activity-dependent modification of the strength of synaptic transmission at pre-existing synapses which plays a central role in the capacity of the brain to convert transient experiences into memory. According to Hebb et al 1, two cells or systems of cells th...
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