The Application Of Machine Learning

Based on Chapter 2, Neural Network Method (NN) will be chosen for voice-based command recognition method because it can handle bigger databased. For Neural Network to implement pattern recognition is quite common, and beneficial to use is backpropagation. Supervised learning that starts by inputting the training data through the network is a form of this method. When the data is put in the network, it will generate propagation output activations and then propagated backwards through the neural network, and generating a delta value for all hidden and output neuron. The weights of the network are then update by calculated delta values that generate by neural network, which increase the speech and quality of the learning process.

Training an artificial Neural Network involves choosing and allowing models for models which there are several associated algorithms.

Data mining is the procedure of getting new patterns from large amount of data. Data mining is a procedure of finding of beneficial information and patterns from huge data. It is also called as knowledge discovery method, knowledge mining from data, knowledge extraction or data/ pattern analysis. The main goal from data mining is to get patterns that were already unknown. The useful of these patterns are found they can be used to make certain decisions for development of their businesses. Data mining aims to discover implicit, already unknown, and potentially useful information that is embedded in data.

Artificial intelligence techniques are increasingly enriching decision support through means as data delivery, analyzing data trends, providing forecasts, developing data consistency, information providing to the exploiter in the most appropriate forms and suggesting courses of action.

Data mining is finding the routines and examples in large databases to guide choices about future exercises. It is normal that data mining tools to get the model with negligible information from the client to identify. Data mining is the utilization of automated data analysis techniques to discover already undetected connections among data things. It regularly determines the

Data Mining is the non-trivial extraction of potentially useful information about data. In other words, Data Mining extracts the knowledge or interesting information from large set of structured data that are from different sources. There are various research domains in data mining specifically text mining, web mining, image mining, sequence mining, process mining, graph mining, etc. Data mining applications are used in a range of areas such as it is used for financial data analysis, retail and telecommunication industries, banking, health care and medicine. In health care, the data mining is mainly used for disease prediction. In data mining, there are several techniques have been developed and used for predicting the diseases

Classification contains finding rules that partition the data into disjoint groups patterns and process. The goal of classification is to evaluate the input data to develop a precise. Explanation or model for each class using the features by using the present data.

It focuses on the relationship between phenomena and data.Ex:The receipt of purchase from supermarket of a customer can reveal many things about the customers such as age,purchasing habits,etc.These could be

In big data analytics, it is necessary to process the data accurately in order to generate appropriate information and knowledge from the input data in order to make appropriate decision for business. Machine learning techniques are used in real applications. The aim of machine learning is to create models, improve the previous models by learning from the accumulated data. Feature extraction is of the important characteristic of machine learning. These features are

Data mining techniques are basically categorised into two major groups as Supervised learning and Unsupervised learning. Clustering is a process of grouping the similar data sets into groups. These groups should have two properties like dissimilarity between the groups and similarity within the group. Clustering is covered in the unsupervised learning category. There are no predefined class label

Traditionally Data Mining is a process of extracting useful knowledge from a large volume of data set. The generated knowledge is applied and used for the most of the applications in all the areas such as science, engineering, business, research, social, health, education, entertainment and all. As all the

Therefore, on the basis of above analysis the weights on the outer layer of unsupervised learning will be used as the output for prediction of future activity of user.

2. Classification Tree Analysis: Statistical classification is a method of identifying categories that a new observation belongs to. It requires a training set of correctly identified observations – historical data in other words.

While machine learning technology is still at infant stages in most industries, it is making ground breaking milestones in the financial sector. Among the areas experiencing major transformations is corporate finance.

If there is much irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase is more difficult. Data preparation and filtering steps can take considerable amount of processing time. Data pre-processing includes cleaning, normalization, transformation, feature extraction and selection, etc. The product of data pre-processing is the final training