Compression Schemes for Mining Large Datasets: A Machine Learning Perspective

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Facebook Instagram Twitter. Sign In Register Help Cart. Cart items. Toggle navigation. Stock photo. Search Results Results 1 -2 of 2. Ravindra Babu. Ships with Tracking Number! These developments encouraged many researchers to work on similar approaches, resulting in two decades of enthusiastic and prolific research in the ML area. In the s, the simplest linear regression model called Ordinary Least Squares OLS —derived from the least squares method [ , ] developed around the s— was used to calculate linear regressions in electro-mechanical desk calculators [ ].

To the best of our knowledge, this is the first evidence of using OLS in computing machines. Following this trend, two linear models for conducting classification were introduced: Maximum Entropy MaxEnt [ , ] and logistic regression [ ]. A different research trend centered on pattern recognition exposed two non-parametric models i. The former uses a distance metric to analyze the data, while the latter applies a kernel function usually, Gaussian to estimate the probability density function of the data.

In the area of clustering, Steinhaus [ ] was the first to propose a continuous version of the to be called k -Means algorithm [ ], to partition a heterogeneous solid with a given internal mass distribution into k subsets. The proposed centroid model employs a distance metric to partition the data into clusters where the distance to the centroid is minimized. In addition, the Markov model [ , ] elaborated 50 years earlier was leveraged to construct a process based on discrete-time state transitions and action rewards, named Markov Decision Process MDP , which formalizes sequential decision-making problems in a fully observable, controlled environment [ 46 ].

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MDP has been essential for the development of prevailing RL techniques [ ]. Research efforts building on the initial NN model flourished too: the modern concept of perceptron was introduced as the first NN model that could learn the weights from input examples [ ]. The perceptron model is also known as Feedforward NN FNN since the nodes from each layer exhibit directed connections only to the nodes of the next layer. An HMM describes the conditional probabilities between hidden states and visible outputs in a partially observable, autonomous environment.

The Baum-Welch algorithm [ 41 ] was proposed in the mis to learn those conditional probabilities. At the same time, MDP continued to instigate various research efforts. The partially observable Markov decision process POMDP approach to finding optimal or near-optimal control strategies for partially observable stochastic environments, given a complete model of the environment, was first proposed by Cassandra et al.

Another development in MDP was the learning automata —officially published in [ ]—, a Reinforcement Learning RL technique that continuously updates the probabilities of taking actions in an observed environment, according to given rewards.

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In , Morgan and Sonquis published Automatic Interaction Detection AID [ ], the first regression tree algorithm that seeks sequential partitioning of an observation set into a series of mutually exclusive subsets, whose means reduces the error in predicting the dependent variable. This algorithm marked the commencement of the Deep Learning DL discipline, though the term only started to be used in the s in the general context of ML, and in the year in the specific context of NNs [ 9 ].

Although ML research was progressing slower than projected in the s [ ], the s were marked by milestones that greatly shaped the evolution of ML, and contributed to its success in the following years.

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Though, BP is widely used in training NNs, its efficiency depends on the choice of initial weights. In particular, BP has been shown to have slower speed of convergence and to fall into local optima. Although CMAC was primarily designed as a function modeler for robotic controllers, it has been extensively used in RL and classification problems for its faster learning compared to MLP.

In , in the area of statistical learning, Dempster et al. In this approach the learning process is driven by the changes, or differences, in predictions over successive time steps, such that the prediction at any given time step is updated to bring it closer to the prediction of the same quantity at the next time step. Towards the end of the s, the second generation of DTs emerged as the Iterative Dichotomiser 3 ID3 algorithm was released.

The algorithm, developed by Quinlan [ ], relies on a novel concept for attribute selection based on entropy maximization. ID3 is a precursor to the popular and widely used C4.

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The s witnessed a renewed interest in ML research, and in particular in NNs. CNN is a feedforward NN specifically designed to be applied to visual imagery analysis and classification, and thus require minimal image preprocessing. Connectivity between neurons in CNN is inspired by the organization of the animal visual cortex —modeled by Hubel in the s [ , ]—, where the visual field is divided between neurons, each responding to stimuli only in its corresponding region. SOMs employ an unsupervised competitive learning approach, unlike traditional NNs that apply error-correction learning such as BP with gradient descent.

Named after the inventor, Hopfield network is an RNN where the weights connecting the neurons are bidirectional. The modern definition of RNN, as a network where connections between neurons exhibit one or more than one cycle, was introduced by Jordan in [ ].

Cycles provide a structure for internal states or memory allowing RNNs to process arbitrary sequences of inputs. Introduced by Hinton in , this concept supports the idea that a system should be represented by many features and that each feature may have different values. Distributed representation establishes a many-to-many relationship between neurons and feature,value pairs for improved efficiency, such that a feature,value input is represented by a pattern of activity across neurons as opposed to being locally represented by a single neuron.

Originally named Harmonium by Smolensky, RBM is a variant of Boltzmann machines [ 2 ] with the restriction that there are no connections within any of the network layers, whether it is visible or hidden.

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Therefor, neurons in RBMs form a bipartite graph. This restriction allows for more efficient and simpler learning compared to traditional Boltzmann machines. RBMs are found useful in a variety of application domains such as dimensionality reduction, feature learning, and classification, as they can be trained in both supervised and unsupervised ways.

The popularity of RBMs and the extent of their applicability significantly increased after the mids as Hinton introduced in a faster learning method for Boltzmann machines called Contrastive Divergence [ ] making RBMs even more attractive for deep learning [ ]. Indeed any of the above NNs can be employed in a DL architecture, either by implementing a larger number of hidden layers or stacking multiple simple NNs.

In addition to NNs, several other ML techniques thrived during the s. REPTree aimed at building faster and simpler tree models using information gain for splitting, along with reduced-error pruning [ ]. For example, TD 0 only updates the estimate of the value of the state preceding the current state. Q-learning, however, replaces the traditional state-value function of TD by an action-value function i.

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Q-value that estimates the utility of taking a specific action in specific states. As of today, Q-learning is the most well-studied and widely-used model-free RL algorithm. By the end of the decade, the application domains of ML started expending to the operation and management of communication networks [ 57 , , ]. Today, LSTM is widely used in speech recognition as well as natural language processing. In DT research, Quinlan published the M5 algorithm in [ ] to construct tree-based multivariate linear models analogous to piecewise linear functions.

One well-known variant of the M5 algorithm is M5P, which aims at building trees for regression models. A year later, Quinlan published C4. Several techniques other than NN and DT also prospered in the s. Research on regression analysis propounded the Least Absolute Selection and Shrinkage Operator LASSO , which performs variable selection and regularization for higher prediction accuracy [ ]. SVM enables plugging different kernel functions e. SVM-based classifiers find a hyperplane to discriminate between categories. A single-class SVM is a binary classifier that deduces the hyperplane to differentiate between the data belonging to the class against the rest of the data, that is, one-vs-rest.

A multi-class approach in SVM can be formulated as a series of single class classifiers, where the data is assigned to the class that maximizes an output function. Unlike Q-learning, SARSA does not update the Q-value of an action based on the maximum action-value of the next state, but instead it uses the Q-value of the action chosen in the next state. A new emerging concept called ensemble learning demonstrated that the predictive performance of a single learning model can be be improved when combined with other models [ ].

As a result, the poor performance of a single predictor or classifier can be compensated with ensemble learning at the price of significantly extra computation.