Tag Info

Why not teach to a NN not only what is true, but also what is not true?

Yes this is done routinely. For example this is how the YOLO object detection and classifier system works, to give a real-world for example. In YOLO, the "non-object" classification is "background" i....
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Where do 'random seeds' get used in deep neural networks?

I suppose the most common part where it will be used is in the initialization of weights before training; the best ways currently known to do that involve randomness. If you use Dropout during ...
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Is a decision tree less suitable for incremental learning than e.g. a neural net?

I will try to answer all three questions to the best of my ability. Is this basically true? Quick googling just brought me to a lot of papers trying to fit decision trees into incremental learning. ...
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Which learning algorithms are suitable for data leakage detection and prevention?

This seems to fall broadly into the regime of a classification problem as you want to classify an outgoing communication as "contains proprietary information" or "does not contain proprietary ...
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How do you interpret this learning curve?

The validation loss settles exactly at an error of one. Probably means there's something off with either the kind of data validation set has or with something in the training. An exact validation loss ...
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How do you interpret this learning curve?

Depends on what does 1 represent in your task. If you are trying to predict household prices and 1 represents \$1, I think the average validation loss is good. If 1 represents \$10000 in this case, ...
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What is the difference between a learning algorithm and a hypothesis?

In computational learning theory, a learning algorithm (or learner) $A$ is an algorithm that chooses a hypothesis (which is a function) $h: \mathcal{X} \rightarrow \mathcal{Y}$, where $\mathcal{X}$ is ...
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What is the proof behind the gradient of a curve being proportional to the distance between the two co-ordinates in the x-axis?

Don’t think about it as the $w_{(n)}-w_{(n+1)}$ being proportional to something. Think about it this way: I'm now at $w_{(n)}$. Where do I want to be at timestep, so that the error decreases? For that,...
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The The Oxford Companion to Chess has entries on only 700 named openings, and lists another 1327 opening variations in the index, and I wouldn't be surprised if someone out there had them all ...
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If you can remember everything and there's no randomisation in your outcome like chess, there is absolutely no reason not to do that. Anybody who can remember all the possible board configurations in ...
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Approaches to an algorithm for crossing a road

Any AI algorithm depends on the environment, and available actuators and sensors. In our case, the environment is a road, street, etc. The primary actuator includes wheels (or legs) of the robot. ...
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Why do Decision Tree Learning Algorithm preferably outputs the smallest Decision Tree?

Adding to SmallChess's answer , Larger trees(with many nodes) are too adapted to the training set, as a small change in the input train data might cause the trees to change very much and hence change ...
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Why do Decision Tree Learning Algorithm preferably outputs the smallest Decision Tree?

The bigger your tree is the more overfitting your model is. In machine learning, we always prefer a simpler model unless there is good reason to go for complication.
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How can artificial intelligence (including deep learning algorithms) find suspicious patterns in the body’s biochemistry?

I wouldn't focus only on "deep learning" unless you have some specific reason for doing so. There may be other techniques which could be as effective, or more effective. One approach I've seen ...
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In the multi-linear regression, how is the value of weight $b_2$ calculated?

It is calculated the same way $b_1$ is calculated. Nearly following your notation, say your multiple linear regression function is $H(X_i) = b_0 + b_1x_{1,i} + ...+ b_nx_{n, i}$ for data ...
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Does a solution for Wumpus World with neural networks exist?

Yes! If you read ahead to the chapters in reinforcement learning in the same book, you'll see that the wompus world appears again there. Techniques like Q-learning can be used to solve it, and since Q-...
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Can we define the AI singularity mathematically?

I found someone that has done this thing! You can hear a good explanation in Marcus Hutter's answer to this question about rewards given to AIXI. He describes a work that seems to be referring to this ...
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Is it possible to do K-nearest-neighbours before training DNN

There are two factors that will change the ability of a deep neural network to fit a given dataset: either you need more data, or a deeper and wider network. Since the pattern is only 2-d, it can ...
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1 vote

What is the approach to deduce formal rules based on data?

If you don't have non-examples of your pattern and don't have some kind of heuristic guide, unfortunately the answer is that you can't. "All sentences" will always be 100% compatible with your ...
1 vote

What's an appropriate algorithm for classification with categorical features?

What you have is called a classification problem with categorical features. That is, the features can be represented numerically, but the numbers have no relative meaning. Algorithms that rely on ...
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1 vote

Teaching a NN to manipulate pseudoRNG over a long time scale?

The point of pseudoRNG is to be unmodable and unpredictable, making it hard to train an AI to learn. It would more likely be useful and more efficient to have the equation that the game uses for ...
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1 vote

Which machine learning algorithm is suitable for detecting text w.r.t set of words

If your main issue is dealing with new vocabulary, you could try using a parts-of-speech tagger as a pre-processing step. You would then effectively discover relationships between "noun" and "verb", ...
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1 vote
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Which is best: evaluation of states or probability of moves?

Which do you think is the best method? As with most machine learning, each approach has its strengths and weaknesses, and other than a little bit of intuition: Policy-based methods are strong in ...
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1 vote

Can we use the recursive least squares as a learning algorithm to an ADALINE?

RLS is a second order optimizer, so, unlike LMS which takes into account an approximation of the derivative of the gradient, RLS also considers the second order derivative. You can study more about ...
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