What are the lexical databases of semantic relations between words for the computer science taxonomies (e.g. artificial intelligence, machine learning, sensor networks, Computer vision, etc) other than WordNet?
Related
The authors have mentioned that the major challenge of the problem is that "large dataset of CT-scan images are not publicly available due to privacy concerns and obtaining very accurate model becomes difficult". As the author proposed the transfer learning based method, the authors should also consider the alternative methods, e.g., weakly supervised methods, methods with conventional texture analysis and coupling of fully convolutional networks and shallow networks:
Hu, Shaoping, et al. "Weakly supervised deep learning for covid-19 infection detection and classification from CT images." IEEE Access 8 (2020): 118869-118883.
Soltaninejad, Mohammadreza, et al. "Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels." Computer methods and programs in biomedicine 157 (2018): 69-84.
Zhang, Lei, Guang Yang, and Xujiong Ye. "Automatic skin lesion segmentation by coupling deep fully convolutional networks and shallow network with textons." Journal of Medical Imaging 6.2 (2019): 024001.
I am not able to understand anything like how I will do please help
Machine Learning - what a hoot!
I have a little project with which I would like to identify anomalies in unlabeled data. Thus, unsupervised clustering.
However, the sequence of the data is also important, as a single record may not be of interest, but the sequence of records that precede it may make it anomalous.
So I am thinking of building a Recurrent SOM to add the temporal context.
I have trained a few simple Machine Learning Models using Python Graphlab Create, Azure Machine Learning and Encog ML Framework, but Azure does not seem to provide unsupervised clustering and I am leaning towards using Encog.
I have looked at Recurrent Neural Networks in Encog, as well as SOM, but I have no idea how to combine the two. Most of the articles online regarding Feedback/Recurrent SOM Machine Learning are mostly academic.
Are there any good references for doing this with Encog?
A google search found only one good answer for RSOM in Encog: https://github.com/leadtune/encog-java/blob/master/encog-core/src/org/encog/neural/pattern/RSOMPattern.java
TextRank is an approach to Automatic Text Summarization. Many categorize it as an "unsupervised" approach. I wish to know if this translates into TextRank being categorized as an Unsupervised Machine Learning technique.
TextRank is not directly related to machine learning: Machine learning involves the creation of a data model to predict future observation based on previous observations. This involves tuning model parameters to fit observed data.
On the other hand, TextRank is a graph-based ranking algorithm: it finds the summary parts based on the structure of a single document and does not use observations to learn anything. Since it's not machine learning, it can't be unsupervised machine learning, either.
The original authors of TextRank, Mihalcea and Tarau, described their work as unsupervised in a sense:
In particular, we proposed and evaluated two innovative unsupervised approaches for keyword and sentence extraction.
However that differs from unsupervised learning, i.e. finding hidden structure within unlabeled data.
Also, TextRank is not a machine learning algorithm, in other words it does not generalize from data by "minimizing a loss function together with a regularization term or side constraints" (per Stephen Boyd, et al.). Linguists might not some similarities, though that's outside the scope of this question.
Even so, some confusion might come from the fact that TextRank and related approaches get used to develop feature vectors to present to machine learning algorithms.
In Wikipedia I found that machine learning is a subsection of neural networks science. So, does it mean that work with machine learning is itself implies working with neural networks or not?
What will be better to use for pattern recognition tasks in terms of efficiency and complexity?
Machine learning is a part of neural network? I'd be surprised because machine learning includes dozen of techniques that have nothing to do with neural network. It's most likely the other way around.
The exact pattern recognition algorithm depends on your requirement and data set. There're many such algorithms, for example, SVM, linear models for classification, HMM, PCA etc. Note that the phase "pattern recognition" is a very general term, there is no algorithm that always work. It all depends on what patterns you are looking and what kind of assumption you can make.
I recommend Dr Bishop's "Pattern Recognition and Machine Learning" book, you'll learn a lot from the book.
recently I started learning pattern recognition and I'm very interested in Perceptron algorithm. But sometimes I heard other students talking about Perceptron network, that is a type of neutral networks. What is the relation ship between them?
I think you should review your terminology first.
It's MULTILAYER, PERCEPTRON, NEURAL network.
That aside, wikipedia is always your friend: MLP
At the highest level, you can think of a MLP as a network of perceptron units (although I'm probably oversimplifying it).