Niche Machine Learning Algorithms

Featuring a sophisticated user-interface, our machine learning as a service provides you with a collaborative platform to build, test and deploy applications without writing codes. The preconfigured algorithms and data handling modules support open-source technologies, helping you automate the model generation and create accurate and efficient models. Moreover, models can be deployed on-premises or in the cloud and tested several times.

At Waveus Networks Pvt Ltd, we believe that ML and Deep learning has the power to disrupt software industry by automating processes and prioritizing routine decision through advanced algorithms such as:

Thus, we provide you the flexibility to infuse continuous AI and ML intelligence in your company processes for a dramatic increase in productivity. We empower you with machine learning services as part of the cloud computing service.

Moreover, you can use PyTorch, TensorFlow, or another framework of your choice. MLaaS data management allows you to store input data for machine learning models in the cloud, import data and even use open datasets. Likewise, model development in the MLaaS platform allows you to develop models from the scratch on any framework and open source Python packages like scikit learn.

With our MLaaS services you can also benefit from bias and Kernel in machine learning. Kernel in machine learning is the method of using a linear classifier for solving a non-linear problem where linearly inseparable data is changed into linearly separable. This technique helps with mapping non-linear observations facilitating separability. Furthermore, it provides a framework for performing pattern analysis on a variety of data for a range of applications.

Likewise, bias in machine learning assists with making models less sensitive to single data point. However, if not monitored properly, it can become susceptible to cognitive biases.

Decision Trees

Decision Trees performs well with the chain of events influencing the outcome. The Tree can be single or Multi-Path, binary events data sets. The tree is traceable to single impactful event at the top. The Business comes up with process and decisions are made recursively Niche ML optimizes the decision trees to produce the outcome. The process Decision could be single structure or trellis structure; both are supported by the Niche ML algorithms.

Naive Bayes Classification

Naive Bayes Classification is used when the Process output is depends on some other independent process or event. The Business Probabilities are one business, connected with multiple process or Business or Events and each of the them may connected with various external factors. These multiple independent factors influence the outcome of the desired business. Niche ML uses parallel event managers and interconnecting them with influencing factors to Learn and Control the Outcome.

Least Squares Regression

Least Square Regression used for best Slope fitting by intercepting available data samples collected form Business or Process. The business and Process may have samples collected over-time with variations within or outside the measuring interval. The Samples are regressed to find-out the best fit slope to operate the decision-making window deterministic. These ML methods are used in small margin errors impacting the manufacturing or Systems.

Logistic Regression

Logistic Regression is statistical model, used when the dependent events are in Binary form. The Logistic regression is used to describe the data set and perform the relationship between one or more dependent binary variable. Each node will have logical decision model moves form one event to another event. The model also, works well with independent variables like nominal, ordinal, time Interval based.

Support Vector Machines

Support Vector Machine Models (SVM) are often used as Supervised Learning Models, uses Associated Learning Process to classify or regress the data set. The System produces the measurement vectors, the known Support Vectors learning sequences were added to classify the separation among the Support Vectors Sequences and data sequences. The Support Vectors are coordinates (Euclidian Vector) to identify the Hyper-Plane from Individual Vectors.

Ensemble Methods

Ensemble Methods Combines Several ML Algorithms to Improve the Prediction or Decrease the variance or Bias on the data set. Ensemble methods considers various internal and external factors associated with different models combinedly produce the outcome. The external factors processed on Edge Computing and Ensemble Methods Processed on Cloud Computing enables faster and time critical results produced in optimized method.

Clustering Algorithms

The Clustering Algorithm Often used to Predict things in Un-Supervised data set. Multiple Clustering algorithms like, K-means, Mean-Shifting, Density-Based Spatial Clustering of Applications with Noise(DBSCAN), Expectation Maximization (EM) + Clustering using Gaussian Mixture Models (GMM)(EM + GM)selected based on the randomness of the Supervised data send fed to the model. The adaptive algorithm selection model in Niche Learning enables optimal selection for Speed and Accuracy.

Principal Component Analysis

Principal Component Analysis (PCA) is Dimensionality Reduction method used in Large dataset. Sampling the dataset often brings-down the accuracy and to address the Sampling errors PCA method is used to segregate the large data set into small Dimensionally reduced data set. The reduction will be based on the amount of accuracy has to be addressed in the data set. The analysis involved multi-dimensional segregation for learning model.

Singular Value Decomposition

Singular Value Decomposition (SVD) based Learning works on Multi-dimensional data like N-D Matrix for faster computation. The diagonal matrix data set is decomposed in diagonally splitting them into left Singular matrix and right singular diagonal. Each diagonal are further decomposed into Eigen vector and Conjugate of Diagonal Component. These models perform well in Matrix data sets which has equal rows and column vectors.

Independent Component Analysis

Independent Component Analysis (ICA) is separate independent non-Gaussian data set. The large data set is separated Independent component set based on the degree of freedom. The source components are separable as Independent components by Minimization of Mutual Information or Maximizations of Gaussian randomness. The minimal dependence among individual components ideal to select the ICA learning model.