Basics of Machine Learning
Machine Learning is a much-talked topic in the software industry and is still received with equal enthusiasm.
With all the high talk one often misses the utility that machine learning can bring to the table. The most simplistic way to look at it is to tell machines how to identify patterns and in effect how to tell what are the objects it is looking at. Once this is done, the mundane tasks can be relegated to machines- robots say separating some machining parts.
The way machine learning structured is by giving the machine some information about the attributes of the object or not giving any features at all and leave it to the machine to identify the characteristics of the object being looked at. This is just classified as supervised and unsupervised learning respectively. To cut the long story short, the basic objective of this programming is to include maximise attributes of an object and remove that cause’s any distortions in identifying the object.
The key challenge with machine learning is that the data on which the decision to identify the object is ambulatory; in the sense of the identification that comes from learning.
Machine Learning Programs at CrafSol:
At Crafsol we are focussed on improvisation of the algorithms after the machines have undergone some experience in identifying objects based on defined attributes. In our experience with larger identifications the rate of the false negatives and positives can get higher and the challenge is to keep it straight. It is here our algorithms can move in tandem with the equidistance of defined attributes in the data sets. With extremely large number of repetitions the identifications get better. Also with large number of repetitions, the attributes of identifying the objects get better. It really becomes an ecosystem that feeds itself.
Key Feature at Crafsol:
We are focussed on the identifying objects that can have definite identifiers and the operating environment is less variable. This has a special significance in manufacturing and stores function and separating materials.
The key to identifying the objects is to appropriately define the object characteristics. The characteristic should not be too wide or narrowly defined to fit the label description. One appropriate characteristics are identified the problem is half solved. We have a team of three resources that do the attribute engineering.
The team working on machine learning has some experience form 6 months- 2 man-years.
The challenge starts after the machine has dealt with a good number of data sets. We strive to develop the algorithms the would include correct attributes by moving statistically closer to the set of desired attributes.
With our learning form some previous projects we have set a process to improvise each nearest neighbour identification. When this coupled with predictive analysis the error rate reduces significantly.
We have come to understand the objective identification should also be reinforced with prediction based on the defined attributes. We have looped this ability with our algorithms so as to have a data set for comparison.
For non-complex and identification like machine parts etc. the attributes can be easily identified defined by photographing the object. It is converted into a points diagram this makes the attributes’ definition easy and the results are consistent and correct with 98% confidence interval. But at the same time the scope of this type of attribute association operates in a limited environment.
We have back to back arrangements for access to a large number of Libraries which our programmers use in developing algorithms. This has implications in various dimensions it reduces the costs, programs use reusable components and the quality is fairly assured as tested modules are used.
We also have access to very lager data sets so that our algorithms can be tested on those large data sets.
IF you have machine learning needs in robotics chat-botics also other academic endeavours please get in touch with our team. Lets get started with things that can be worked on.