This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering , and allows a machine to both learn the features and use them to perform a specific task.
Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks , multilayer perceptrons , and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis , autoencoders , matrix factorization  and various forms of clustering.
Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.
Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.
Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions , and is assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately.
Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine to which classes a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot.
In data mining , anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Anomalies are referred to as outliers , novelties, noise, deviations and exceptions.
In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods in particular, unsupervised algorithms will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns.
Three broad categories of anomaly detection techniques exist. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection. Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then test the likelihood of a test instance to be generated by the model.
Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness". Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system.
This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements.
In addition to market basket analysis , association rules are employed today in application areas including Web usage mining , intrusion detection , continuous production , and bioinformatics. In contrast with sequence mining , association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems LCS are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm , with a learning component, performing either supervised learning , reinforcement learning , or unsupervised learning.
They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.
Introduction to Statistical Relational Learning
Inductive logic programming ILP is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples.
Inductive programming is a related field that considers any kind of programming languages for representing hypotheses and not only logic programming , such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting. Performing machine learning involves creating a model , which is trained on some training data and then can process additional data to make predictions.
Various types of models have been used and researched for machine learning systems. Artificial neural networks ANNs , or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is a model based on a collection of connected units or nodes called " artificial neurons ", which loosely model the neurons in a biological brain.
Each connection, like the synapses in a biological brain , can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number , and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs.
The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers.
Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer the input layer , to the last layer the output layer , possibly after traversing the layers multiple times.
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The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology.
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Artificial neural networks have been used on a variety of tasks, including computer vision , speech recognition , machine translation , social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition. Decision tree learning uses a decision tree as a predictive model to go from observations about an item represented in the branches to conclusions about the item's target value represented in the leaves.
It is one of the predictive modeling approaches used in statistics, data mining and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels.
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Decision trees where the target variable can take continuous values typically real numbers are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines SVMs , also known as support vector networks, are a set of related supervised learning methods used for classification and regression.
Introduction to Statistical Relational Learning Hardcover - ocriphogeabpi.ga
Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick , implicitly mapping their inputs into high-dimensional feature spaces.
A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph DAG. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms.
Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences , are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. A genetic algorithm GA is a search algorithm and heuristic technique that mimics the process of natural selection , using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem.
In machine learning, genetic algorithms were used in the s and s. Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service.
Overfitting is something to watch out for when training a machine learning model. Federated learning is a new approach to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server.
This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google. Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.