#SupportVectorMachines

[govindhtech]

Quantum Kernel Methods In Quantum ML For IoT Data Analytics

IoT Data Prediction Improves with Quantum Machine Learning Kernels.

Quantum kernel techniques

Recent research examines how quantum computation could improve the processing and interpretation of the growing volume of data produced by networked IoT devices. Scientists are curious if quantum kernel approaches can improve machine learning tasks like categorising and forecasting data. The use of projected quantum kernels (PQKs) to classify IoT data has been extensively studied by Francesco D'Amore and colleagues.

Beyond kernel approaches, quantum machine learning

Machine learning has great opportunities and challenges as IoT data grows. The study team focused on constructing prediction models with a quantum algorithm-compatible dataset to tackle this. This method avoided the lengthy pre-processing needed to adapt classical datasets to quantum methods.

The study focused on quantum kernel approaches. Kernel approaches tackle problems by implicitly mapping information into a higher-dimensional space. The Projected Kernel (PQK) quantum algorithm encodes data into a Hilbert space, which represents all quantum system states. For analysis, this quantum form is projected onto classical space. This method uses quantum computational principles without data organised for quantum processing.

This study uses a real IoT dataset. Even though many quantum machine learning studies use fabricated or simplified data, realistic issues must be used to assess quantum approaches' applicability. The dataset, a representative sample of smart building data, contained sensor readings of office ambient conditions.

This dataset can also be used with quantum algorithms without complex dimensionality reduction. For better accuracy and processing efficiency, choose a directly compatible dataset. The research shows how these methods might improve smart building occupancy forecast accuracy, addressing a major difficulty in quantum machine learning: the lack of quantum-compatible datasets.

The study stressed proper feature maps. Feature maps are needed to efficiently encapsulate classical data from IoT devices for quantum computation. These maps transform raw data for machine learning. Feature map selection strongly impacts model performance and quantum algorithm data learning. The study examined how many feature maps encoded conventional IoT data into a quantum state to investigate how different encoding strategies affect learning and generalisation.

A PQK approach

Research team benchmarked PQK method extensively. They compared its performance to classical kernel methods and Support Vector Machines. Understanding the quantum technique's pros and cons and assessing its efficacy requires these comparisons. The results suggest that PQK may increase prediction performance, laying the groundwork for comparison with classical methods.

The study found that PQK improves IoT data prediction, although the researchers urged further research. Future research should focus on scaling these algorithms to handle larger and more complex datasets. PQK's noise resistance, a property of near-term quantum technology, must also be examined.

This quantum-inspired strategy will be tested by expanding IoT applications beyond smart buildings and comparing performance to deep neural networks.

The study “Assessing Projected Quantum Kernels for the Classification of IoT Data” describes these findings. Information is more accessible.

This study covers classical machine learning, dataset creation, feature maps, Hilbert space, IoT devices, Quantum kernel approaches, prediction models, projected kernel, quantum algorithms, and quantum machine learning. This work advances quantum machine learning by showing how quantum computing could transform many sectors.

[tccicomputercoaching]

Understand SVM – a powerful ML algorithm for classification and regression. Learn with TCCI Ahmedabad.

[outliersxx]

[phungthaihy]

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[royalvoxpost]

#MachineLearning algorithms could help linguists and literary analysts identify ghostwriters or uncredited collaborators in famous literary works - https://t.co/vZ0stzrI3d #SupervisedLearning #SupportVectorMachine #Literature https://t.co/V7yoCWqgpk

#MachineLearning algorithms could help linguists and literary analysts identify ghostwriters or uncredited collaborators in famous literary works - https://t.co/vZ0stzrI3d #SupervisedLearning #SupportVectorMachine #Literature pic.twitter.com/V7yoCWqgpk

— The Royal Vox Post (@RoyalVoxPost) November 22, 2019

via Twitter https://twitter.com/RoyalVoxPost November 22, 2019 at 09:33PM

[omarkhanca]

machine learning > SupportVectorMachines - Coursera Advanced Machine Learning | 2018-05-20T22:34:12.000Z

[tccicomputercoaching]

SVM Algorithm Explained – TCCI Ahmedabad

Support Vector Machines (SVMs) are supervised machine learning algorithms employed in classification and regression tasks.

Hyperplane: A hyperplane separates data points of different classes in feature space; it can also be called a decision boundary.

Mathematical Representation: w•x + b = 0

Example:

For instance, if we will classify animals as cat (+1) and dog (-1) by weight and height, then the hyperplane could be

3×Weight + 2×Height - 50 = 0

Support Vectors: These data points are the nearest ones to hyperplane. These points influence the orientation and position of hyperplane.

Margin: The margin is the distance between hyperplane and the nearest data point of either class; SVM tries to maximize margin to increase confidence of classifications.

Hard Margin: Assuming that the data is perfectly separable by hyperplane. Therefore, all points must lie outside of the margin.

Soft Margin: It allows a few misclassifications or margin violations for non-linearly separable data.

Kernel Function: Kernel function transforms the data into higher dimensional space to make it linearly separable.

Types of Kernels:

Linear Kernel: Suitable for linearly separable data.

Polynomial Kernel: For curved boundaries.

Radial Basis Function (RBF): Captures complex relationships.

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[expertlytics]

———————————————————————— SVMs are linear classifiers which assume that your data are linearly separable[1].  In the simplified two-dimensional case, imagine that your data are plotted along the x- and y-axes.  The algorithm attempts to find an optimal vector (in this case, a line) that separates them.  Indeed, the algorithm would like to separate the instances by a maximum margin.  This is so that unseen examples are more likely to be classified correctly. In higher dimensional spaces, the algorithm searches for a hyperplane, but the principle is the same. - A technique known as the "kernel trick" allows SVMs to be used on non-linearly separable data. ———————————————————————— #data #datascience #datascientist #datavisualization #dataviz #datamining #machinelearning #artificialintelligence #algorithm #engineering #math #mathematics #statistics #studygram #learning #study #visualization #simulation #science #computerscience #mathconcepts #research #computers #supportvectormachines #svm #математика #classification #supervisedlearning #наука (at United States)